In particular , we show that Laplace-based importance sampling and Laplace-based quasi-Monte-Carlo methods are robust w.r.t . the concentration of the posterior for large classes of posterior distributions and integrands whereas prior-based importance sampling and plain quasi-Monte Carlo are not . Numerical experiments are presented to illustrate the theoretical findings . The invention discloses an improved particle filter track-before-detection method for weak target detection . In the method , The method is characterized in that : particles are divided into three types of sets of death , new birth and continuing survival according to the state of a target existence variable , and the state of particles in different sets are separately operated ; the particles in the death set are not defined or operated ; the particles in the new birth set acquire a new state according to prior distribution sampling ; and the particles in the continuing survival set are regenerated according to gaussian distribution and acquire a new state according to a target motion model . By the method , the amount of operation is reduced , the diversity of the particles is improved , and the phenomenon of sampling exhaustion is effectively avoided . The history of each component 's associate weights is stored in a multi-scan sliding window , which is used to judge whether the component is from a target or clutter . Moreover , a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step . From the simulation results , it could be seen that the proposed algorithm can efiectively detect the appearance/disappearance of the targets as well as track the existing target . However , alternative methods are required when collocated training sample data are unavailable or sparse but non-collocated data collected at sample units not aligned across time exist or can be made available more conveniently and flexibly . This is typically the case for change-classification accuracy analyses , whereby prior sampling design and sample data may not remain informative in time but can be made adaptive by restructuring and augmentation to better represent strata of transitions as well as persistence . Such adaptability leads to great cost-saving in sampling than undertaking collocated sampling anew each time performing or revising accuracy mapping for a specific change analysis over a particular period . The developed biosensor showed high selectivity in the presence of Escherichia coli and Staphylococcus aureus . It provides an alternative platform for rapid and specific detection of S. Typhimurium without any prior sampling processes . The proposed biosensing technique could be used for sensitive detection ( 275.86 nm/RIU ) of the broad spectrum of pathogens . The features extracted from these methods will be fed into an RFR both individually and combined , using the union and intersection operators . The features given by the IPCA ∪ PCA ∪ KPCA method , using prioritized sampling with multiple features per day provide the best improvement over the baseline . This paper uses fuzzy clustering to extend a previous prioritized sampling proposal . In many big data problems , modeling an individual object such as a large engineering plant can be a tedious process requiring up to a month of analysis . We introduce an adaptive learning mechanism that allows the algorithm to find feasible solutions even for tasks that would otherwise be intractable . Furthermore , in order to improve the performance on difficult objects , we use a prioritized sampling scheme . We validate the efficacy of our approach with a real robot in a pick and lift task of different objects . Without bells and whistles , our method achieves SOTA reconstruction accuracy and competitive validity on the ZINC 250 K dataset . When generating 10,000 unique valid SMILES from random prior sampling , it costs JT-VAE1450s while our method only needs 9s . Our implementation is at this https URL . The agent would then sample a mini - batch uniformly at random from the replay buffer . Drawing inspiration from prioritized sweeping moore1993prioritized , prioritized experience replay replaces the uniform sampling with prioritized sampling proportional to the absolute TD error schaul16prioritized . Specifically , for a replay buffer of size , prioritized experience replay samples transition with probability , and applies weighted importance - sampling with to correct for the prioritization bias , where Prioritized DQN significantly increases both the sample - efficiency and final performance over DQN on the Atari 2600 benchmarks schaul2015prioritized . In this section we present an alternative initialization strategy , called lazy initialization , and argue that it better encodes prior information about temporal difference errors . We then briefly describe our computationally efficient prioritized sequence sampling algorithm , with full details left to the appendix . It is widely recognized that TD errors tend to be temporally correlated The remaining sentences are generated by the reverse NMT model . Inspired by edunov2018understanding , we use greedy decoding for half of them , which produces more fluent and predictable translations , and random sampling for the other half , which produces more varied translations . In our experiments , we use and , and perform a total of 60 such iterations . Starting from a single entry point in the top level we simulate the backtracking procedure from Section by replacing in Eq . ( ) the max operation by a random sampling over { - 1 , 0 , 1}2 . By generating 10 , 000 sets of possible atomic correspondences , we simulate a set which respects the deformations allowed by DeepMatching . The second one is sampled from and serves as input to the generator along with a latent vector to generate an image , and it can be sampled using different approaches : Kernel density estimation : also known as Parzen window estimation , it consists in randomly sampling from a kernel ( e.g. Gaussian kernel with a cross - validated ) . Direct interpolation : interpolate between label vectors from the training set Reed2016 . Unless otherwise mentioned , we use for all our models . It is worth mentioning that we also experimented with stochastic depth by randomly sampling during training , but we observed no improvement in validation accuracy . Parameter sharing : We empirically find parameter sharing to be crucial for training a deep recursive model . Compared with conventional airport detection methods which design specfic low-level manually designed features for high-resolution remote sensing images , an end-to-end network can mine the general characteristic among the training samples and learn high-level features in multi-scale and multi-view remote sensing images . Meanwhile , an automatic hard example mining principle is introduced to make training more efficiently and accurately . The proposed method is validated on a multi-scale and multi-view dataset collected from Google Earth . Then , hard example mining in tumor segmentation is also explored . Finally , experiments are conducted for evaluating segmentation performance of our method , demonstrating the effects of our method and hard example mining . Meanwhile , we incorporate the recurrent neural network layers in the proposed network to refine the predicted results . Specifically , hard example mining is performed to train the STDN with cascade learning , which has a remarkable improvement in precision . Besides , we exploit a clustering algorithm to generate anchor dimensions spontaneously without hand-picking , which is portable and time-saving . While much effort has been spent on applying deep learning to vehicle detection , the effective use of training data has not been thoroughly studied , although it has great potential for improving training results , especially in cases where the training data are sparse . In this paper , we proposed using hard example mining ( HEM ) in the training process of a convolutional neural network ( CNN ) for vehicle detection in aerial images . We applied HEM to stochastic gradient descent ( SGD ) to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses . We show that our pRoI generator is able to simulate other sampling methods for positive examples such as hard example mining and prime sampling . Using our generator as an analysis tool , we show that ( i ) IoU imbalance has an adverse effect on performance , ( ii ) hard positive example mining improves the performance only for certain input IoU distributions , and ( iii ) the imbalance among the foreground classes has an adverse effect on performance and that it can be alleviated at the batch level . Finally , we train Faster R-CNN using our pRoI generator and , compared to conventional training , obtain better or on-par performance for low IoUs and significant improvements when trained for higher IoUs for Pascal VOC and MS COCO datasets . We propose a new feature fusion module in each network to explore complex nonlinear predictors and improve accuracy . Then , we propose a novel loss named semi-focal loss and we use off-line hard example mining to solve the problem of class imbalance and error labeling . Finally , we propose a new training skill of cutting patches in the whole slide image , considering the size and distribution of mitotic cells . In this work , we propose to combine the residual network ( ResNet ) with the self-attention mechanism to achieve better performance in text-independent speaker recognition with fewer parameters and lower computational cost . In addition , the Cluster-Range Loss based on a well-designed online exemplar mining is proposed to directly shrink the intra-class variation and to enlarge the inter-class distance . Experiments on Voxceleb dataset are conducted to verify the effectiveness of the proposed scheme . Second , a strategy of horizontal region generation is proposed to fully utilize the aligned property of widget arrangement , which generates all the region candidates in a horizontal line at once . Finally , a variant of online hard example mining is employed to alleviate the problem of imbalance samples , which explicitly restricts the ratio of foreground and background to achieve better balance . We conduct experiments on a proposed benchmark dataset . In this report , we propose a robust deep face detection approach based on Faster R-CNN . In our approach , we exploit several new techniques including new multi-task loss function design , online hard example mining , and multi-scale training strategy to improve Faster R-CNN in multiple aspects . The proposed approach is well suited for face detection , so we call it Face R-CNN . In this paper , we present a novel approach that learns a semantic connection between pedestrian head and other body parts for head detection . Specifically , the proposed model , named as HeadNet , is based on PVANet backbone and also introduces beneficial strategies including online hard example mining ( OHEM ) , fine-grained feature maps , RoI Align and Body in Context ( BiC ) . Experiments demonstrate that our approach is able to utilize spatial semantics of the entire body effectively , and gains inspiring performance for pedestrian head detection . The two-stream network is jointly optimized by fusing the flow and RGB feature maps at different levels . Additionally , the training stage incorporates an online hard example mining strategy to address the extreme foreground-background imbalance typically observed in any detection pipeline . Instead of heuristically sampling the candidate segments for the final activity classification stage , we rank them according to their performance and only select the worst performers to update the model . We show that our optimization method can be interpreted as a principled Hard Example Mining strategy . Similar to an online hard example mining strategy in essence and in practice , the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning . It only requires adding a softmax layer and maintaining a history of the loss values for each training example to compute adaptive sampling probabilities . Considering the highly varying shape of atrium and the branchy structure of associated pulmonary veins , we propose to adopt a pyramid module to collect semantic cues in feature maps from multiple scales for fine-grained segmentation . Also , to promote our network in classifying the hard examples , we propose an Online Hard Negative Example Mining strategy to identify voxels in slices with low classification certainties and penalize the wrong predictions on them . Finally , we devise a competitive training scheme to further boost the generalization ability of networks . To generate nodule candidates , we build a full 3D CNN model that employs 3D U-Net architecture as the backbone of a region proposal network ( RPN ) . We adopt multi-task residual learning and online hard negative example mining strategy to accelerate the training process and improve the accuracy of nodule detection . Then , a 3D DenseNet-based model is presented to reduce false positive nodules . subsection : Hard example mining Hard example mining is an important strategy to improve model quality , and has been studied extensively in image classification and general object detection . The main idea is to find some hard positive and hard negative examples at each step , and put more effort into training on those hard examples . We use a smaller threshold ( 0.3 ) because ( 1 ) this will allow imperfectly matched anchors to be able to localize the face , which may be useful during the testing and ( 2 ) the regression task has less supervision since unlike classification , there are no negative anchors for computing loss and the positive anchors are usually sparse . subsubsection : Anchor - level hard example mining OHEM has been proven to be useful for object detection and face detection in . document : Training Region - based Object Detectors with Online Hard Example Mining The field of object detection has made significant advances riding on the wave of region - based ConvNets , but their training procedure still includes many heuristics and hyperparameters that are costly to tune . Since these models do not use SVMs , and are trained purely online with SGD , existing hard example mining techniques can not be immediately applied . This work addresses that problem by introducing an online hard example mining algorithm that improves optimization and detection accuracy . We briefly review hard example mining , modern ConvNet - based object detection , and relationships to concurrent works using hard example selection for training deep networks . paragraph : Hard example mining . There are two hard example mining algorithms in common use . The first is used when optimizing SVMs . subsection : Online hard example mining Recall the alternating steps that define a hard example mining algorithm : ( a ) for some period of time a fixed model is used to find new examples to add to the active training set ; ( b ) then , for some period of time the model is trained on the fixed active training set . In the context of SVM - based object detectors , such as the SVMs trained in R - CNN or SPPnet , step ( a ) inspects a variable number of images ( often 10 ’s or 100 ’s ) until the active training set reaches a threshold size , and then in step ( b ) the SVM is trained to convergence on the active training set . Furthermore , there are many patterns similar to text strokes , such as fences , lattices , etc . Therefore , we adopt Online Hard Example Mining ( OHEM ) to during training to better distinguish these patterns . Automatic selection of these hard examples can make training more effective and efficient . OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use . But more importantly , it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012 . A lot of methods have been proposed to improve the performance of DNNs , such as the ReLU ( Rectified Linear Unit ) which is a widely used alternative of the sigmoid activation function , Local Response Normalization and Batch Normalization . What 's more , some methods , OHEM ( Online Hard Sample Mining ) for instance , aims to augment the capability of generalization of DNNs in certain scenarios . In this paper , we choose the LeNet architecture as our baseline model , which is known to work well on digit classification tasks . In this paper , we present the Stratified Online Hard Example Mining ( S-OHEM ) algorithm for training higher efficiency and accuracy detectors . S-OHEM exploits OHEM with stratified sampling , a widely-adopted sampling technique , to choose the training examples according to this influence during hard example mining , and thus enhance the performance of object detectors . We show through systematic experiments that S-OHEM yields an average precision ( AP ) improvement of 0.5 % on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7 . For KITTI 2012 , both results of the same metric are 1.6 % . Regarding the mean average precision ( mAP ) , a relative increase of 0.3 % and 0.5 % ( 1 % and 0.5 % ) is observed for VOC07 ( KITTI12 ) using the same set of IoU threshold . Also , S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors . Thirdly , we present a multi-stage image-based online hard example mining ( MSI-OHEM ) framework specifically designed for single-stage detectors . MSI-OHEM performs fine-tuning on hard examples and the ones with slightly-insufficient IOU that are considered true positives . Compared to other classical object detectors , the proposed detector achieves very competitive result in terms of average precision ( AP ) and computational speed . To address this issue , in this paper , we propose a robust face detector by putting more training focus on those hard images . This issue is most related to anchor - level hard example mining discussed in OHEM . However , due to the sparsity of ground - truth faces and positive anchors , traditional anchor - level hard example mining mainly focuses on mining hard negative anchors , and mining hard anchors on well - detected images exhibits less effectiveness since there is no useful information that can be further exploited in these easy images . But these problems are not existed for two - stage detectors , owing to the proposal - driven mechanism . To handle the former imbalance problem , example mining based methods such as OHEM are in common use , but they directly abandon most examples and the training is inefficient . For the latter imbalance , the recent work , Focal Loss , has tried to address it by rectifying the cross - entropy loss function to a elaborately designed form . Automatic water feature delineation from satellite imagery is a challenging task due to the variability of sensor parameters , acquisition conditions , and geographical heterogeneity of scenes . To mitigate above challenges , we proposed a fully automatic method for surface water feature dilatation from the moderate resolution satellite imagery through the integration of adaptive and automatic training sample collection by a modulated spectral reflectance curve and one-class classification algorithm . The specific modulation codes that are partially able to represent water features have been chosen to collect positive training samples for the target class . By virtue of the ability to reveal the non-linear structure hidden in the high-dimensional image space , LPP is an efficient manifold learning method to analyze the local intrinsic features on the manifold of local facial areas . By searching out patches online in the LPP sub-space , which makes the resultant training set tailored to the testing patch , our algorithm performed the adaptive sample selection and then effectively restored the lost high-frequency components of the low-resolution face image by patch-based eigen transformation using the dynamic training set . Finally , experiments fully demonstrated that the proposed method can achieve good performance of super-resolution reconstruction by utilizing a relative small sample . To perform interactive pattern analysis , we propose two online pattern classification methods , called interactive random forests ( IRF ) and adaptive random forests ( ARF ) , that adapt a composite classifier known as random forests for relevance feedback . IRF improves the efficiency of regular random forests ( RRF ) with a novel two-level resampling technique called biased random sample reduction , while ARF boosts the performance of RRF with two adaptive learning techniques called dynamic feature extraction and adaptive sample selection . During interactive multimedia retrieval , both ARF and IRF run two to three times faster than RRF while achieving comparable precision and recall against the latter . This research proposes a systematic methodology to develop an effective online evolving PHM method with adaptive sampling mechanism against continuous stream data . An adaptive sample selection strategy is developed to effectively select the representative samples in both off-line and online environment . Meanwhile , a probabilistic theory-based modeling approach is developed to update the model with newly selected samples . The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images . An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point . Thus , each patch can preserve the texture structure around its key point rather than all patches have a predetermined size . Here a conflict arises : More data will increase the reliability of the prediction model but will also increase the costs and the time necessary for the variation study . This paper reports about the utilization of gaussian process based machine learning that can handle the described conflict by enabling an adaptive sampling of training data . In order to improve the reconstruction accuracy and reduce the workload , the algorithm of compressive sensing based on the iterative threshold is combined with the method of adaptive selection of the training sample , and a new algorithm of adaptive compressive sensing is put forward . The three kinds of training sample are used to reconstruct the spectral reflectance of the testing sample based on the compressive sensing algorithm and adaptive compressive sensing algorithm , and the color difference and error are compared . The simplified multi-objective optimisation model is then solved by the combination of a multi-objective evolutionary algorithm called $ { \rm \epsilon } $ e-NSGAII and extreme learning machine-based surrogate modelling with adaptive training data sampling . This combination significantly reduces the unaffordable computing burden . A two-stage data association is first developed to handle the drifting targets stemming from occlusions and people 's abrupt motion changes . Subsequently , a novel online appearance learning is developed by using the incremental/decremental support vector machine with an adaptive training sample collection strategy to ensure reliable data association and rapid learning . Experimental results show the effectiveness and robustness of the proposed method while demonstrating its compatibility with real-time applications . This shows that how to select positive and negative training samples is important for current object detectors . Then , we propose an Adaptive Training Sample Selection ( ATSS ) to automatically select positive and negative samples according to statistical characteristics of object . It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them . Aircraft tractor supplier selection ( ATSS ) is critical for equipment procurement management in airport ground handling service ( AGHS ) companies because it guarantees the work safety of airport ramps . Although AGHS ATSS plays a crucial role in the safety of ground handling ramp operations , few studies have analyzed the ATSS of AGHS companies . This paper describes a new approach to compare the Delphi method , analytic hierarchy process , involvement cost-factor measures , and multi-choice goal programming by considering both financial and nonfinancial factors for ATSS from a financial perspective . It allows the network to compute the correlation between each feature parallelly , and using shallower layers to extract more features , compared with the CNN-LSTM architecture . Experimental results show that our Atss-Net yields better performance than the VoiceFilter , although it only contains half of the parameters . Furthermore , our proposed model also demonstrates promising performance in speech enhancement . ATS was the best design according to these measurements . We then compared ATS , adaptive cluster sampling ( ACS ) , adaptive two-stage sequential sampling ( ATSS ) , and the currently used stratified random sampling design . ATS performed better than the other two approaches and the current stratified random sampling design . Additionally , this work copes with a novel optimization method combining multi-objective optimization with a grammatical evolution methodology . We implemented the ATS testing three different fitness functions under three mono-objective approaches and also two multi-objective ATSs . Experimental results test and compare them to the Buy and Hold strategy and a previous approach , beating both in returns and in number of positive operations . The analysis was carried out descriptively to produce elements that support low energy criteria . Testing low energy elements using the Analytical Hierarchy Process ( AHP ) method to determine the weight of the priority elements of traditional low energy ATSS architectural design . The research results obtained are important factors in the ATSS design ( bale/ traditional house , berugak , and geleng/barn ) in Traditional Village Senaru , where low energy conceptors are site design factors ( orientation and land cover/pavement , geo-biological disturbances , vegetation , topography and type of land , and guardrail ) that are integrated in the green concept based on suitability and carrying capacity of the site and landscape . As a building block for constructing ATSS , we also introduce the notion of accountable chameleon hash ( ACH ) , which is an extension of chameleon hash ( CH ) and might be of independent interest . We propose a concrete construction of ACH and show how to use it to construct an ATSS scheme . A Domain-Specific Software Architecture ( DSSA ) could bring a large-scale reuse of software in the construction of application systems . Although there are several typical Avionics Testing Systems ( ATSs ) all over the world , the DSSA of ATS ( DSSAATS ) has never been put forward . ATS , as a software-intensive system , is such a system that can effectively be used to implement automatic , realtime , close-loop and non-invasive system testing for avionics . The other example shows how the model set up enables strategies for dealing tool specific CD signature differences . In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget . Overall , this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing . To fulfil all these constraints and to optimally use the available prior information , we propose a multi-stage self-weighted unequal probability sampling design . The design uses modern techniques such as : balanced sampling , spreading , stratified balancing , calibration , unequal probability sampling and power allocation . This sampling design meets the numerous requirements of this study and provides a very efficient estimator . To mitigate the adverse effects caused thereby , we propose Libra R-CNN , a simple but effective framework towards balanced learning for object detection . It integrates three novel components : IoU-balanced sampling , balanced feature pyramid , and balanced L1 loss , respectively for reducing the imbalance at sample , feature , and objective level . Benefitted from the overall balanced design , Libra R-CNN significantly improves the detection performance . The top accuracy of object detection to date is led by region-based approaches , where the per-region stage is responsible for recognizing proposals generated by the region proposal network . In that stage , sampling heuristics ( e.g. , OHEM , IoU-balanced sampling ) is always applied to select a part of examples during training . But nowadays , existing samplers ignore the overlaps among examples , which may result in some low-quality predictions preserved . Our implementation was done in Torch . We adopt the balanced sampling from , by sampling from context dependent state with probability . We keep throughout the experiments during cross - entropy training .
In particular , we show that Laplace-based importance sampling and Laplace-based quasi-Monte-Carlo methods are robust w.r.t . the concentration of the posterior for large classes of posterior distributions and integrands whereas prior-based importance sampling and plain quasi-Monte Carlo are not . Numerical experiments are presented to illustrate the theoretical findings .
The invention discloses an improved particle filter track-before-detection method for weak target detection . In the method , The method is characterized in that : particles are divided into three types of sets of death , new birth and continuing survival according to the state of a target existence variable , and the state of particles in different sets are separately operated ; the particles in the death set are not defined or operated ; the particles in the new birth set acquire a new state according to prior distribution sampling ; and the particles in the continuing survival set are regenerated according to gaussian distribution and acquire a new state according to a target motion model . By the method , the amount of operation is reduced , the diversity of the particles is improved , and the phenomenon of sampling exhaustion is effectively avoided .
The history of each component 's associate weights is stored in a multi-scan sliding window , which is used to judge whether the component is from a target or clutter . Moreover , a novel sampling method which combines the likelihood sampling and prior sampling is proposed to draw particles from the desired parts of the state space at each time step . From the simulation results , it could be seen that the proposed algorithm can efiectively detect the appearance/disappearance of the targets as well as track the existing target .
However , alternative methods are required when collocated training sample data are unavailable or sparse but non-collocated data collected at sample units not aligned across time exist or can be made available more conveniently and flexibly . This is typically the case for change-classification accuracy analyses , whereby prior sampling design and sample data may not remain informative in time but can be made adaptive by restructuring and augmentation to better represent strata of transitions as well as persistence . Such adaptability leads to great cost-saving in sampling than undertaking collocated sampling anew each time performing or revising accuracy mapping for a specific change analysis over a particular period .
The developed biosensor showed high selectivity in the presence of Escherichia coli and Staphylococcus aureus . It provides an alternative platform for rapid and specific detection of S. Typhimurium without any prior sampling processes . The proposed biosensing technique could be used for sensitive detection ( 275.86 nm/RIU ) of the broad spectrum of pathogens .
The features extracted from these methods will be fed into an RFR both individually and combined , using the union and intersection operators . The features given by the IPCA ∪ PCA ∪ KPCA method , using prioritized sampling with multiple features per day provide the best improvement over the baseline .
This paper uses fuzzy clustering to extend a previous prioritized sampling proposal . In many big data problems , modeling an individual object such as a large engineering plant can be a tedious process requiring up to a month of analysis .
We introduce an adaptive learning mechanism that allows the algorithm to find feasible solutions even for tasks that would otherwise be intractable . Furthermore , in order to improve the performance on difficult objects , we use a prioritized sampling scheme . We validate the efficacy of our approach with a real robot in a pick and lift task of different objects .
Without bells and whistles , our method achieves SOTA reconstruction accuracy and competitive validity on the ZINC 250 K dataset . When generating 10,000 unique valid SMILES from random prior sampling , it costs JT-VAE1450s while our method only needs 9s . Our implementation is at this https URL .
The agent would then sample a mini - batch uniformly at random from the replay buffer . Drawing inspiration from prioritized sweeping moore1993prioritized , prioritized experience replay replaces the uniform sampling with prioritized sampling proportional to the absolute TD error schaul16prioritized . Specifically , for a replay buffer of size , prioritized experience replay samples transition with probability , and applies weighted importance - sampling with to correct for the prioritization bias , where Prioritized DQN significantly increases both the sample - efficiency and final performance over DQN on the Atari 2600 benchmarks schaul2015prioritized .
In this section we present an alternative initialization strategy , called lazy initialization , and argue that it better encodes prior information about temporal difference errors . We then briefly describe our computationally efficient prioritized sequence sampling algorithm , with full details left to the appendix . It is widely recognized that TD errors tend to be temporally correlated
The remaining sentences are generated by the reverse NMT model . Inspired by edunov2018understanding , we use greedy decoding for half of them , which produces more fluent and predictable translations , and random sampling for the other half , which produces more varied translations . In our experiments , we use and , and perform a total of 60 such iterations .
Starting from a single entry point in the top level we simulate the backtracking procedure from Section by replacing in Eq . ( ) the max operation by a random sampling over { - 1 , 0 , 1}2 . By generating 10 , 000 sets of possible atomic correspondences , we simulate a set which respects the deformations allowed by DeepMatching .
The second one is sampled from and serves as input to the generator along with a latent vector to generate an image , and it can be sampled using different approaches : Kernel density estimation : also known as Parzen window estimation , it consists in randomly sampling from a kernel ( e.g. Gaussian kernel with a cross - validated ) . Direct interpolation : interpolate between label vectors from the training set Reed2016 .
Unless otherwise mentioned , we use for all our models . It is worth mentioning that we also experimented with stochastic depth by randomly sampling during training , but we observed no improvement in validation accuracy . Parameter sharing : We empirically find parameter sharing to be crucial for training a deep recursive model .
Compared with conventional airport detection methods which design specfic low-level manually designed features for high-resolution remote sensing images , an end-to-end network can mine the general characteristic among the training samples and learn high-level features in multi-scale and multi-view remote sensing images . Meanwhile , an automatic hard example mining principle is introduced to make training more efficiently and accurately . The proposed method is validated on a multi-scale and multi-view dataset collected from Google Earth .
Then , hard example mining in tumor segmentation is also explored . Finally , experiments are conducted for evaluating segmentation performance of our method , demonstrating the effects of our method and hard example mining .
Meanwhile , we incorporate the recurrent neural network layers in the proposed network to refine the predicted results . Specifically , hard example mining is performed to train the STDN with cascade learning , which has a remarkable improvement in precision . Besides , we exploit a clustering algorithm to generate anchor dimensions spontaneously without hand-picking , which is portable and time-saving .
While much effort has been spent on applying deep learning to vehicle detection , the effective use of training data has not been thoroughly studied , although it has great potential for improving training results , especially in cases where the training data are sparse . In this paper , we proposed using hard example mining ( HEM ) in the training process of a convolutional neural network ( CNN ) for vehicle detection in aerial images . We applied HEM to stochastic gradient descent ( SGD ) to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses .
We show that our pRoI generator is able to simulate other sampling methods for positive examples such as hard example mining and prime sampling . Using our generator as an analysis tool , we show that ( i ) IoU imbalance has an adverse effect on performance , ( ii ) hard positive example mining improves the performance only for certain input IoU distributions , and ( iii ) the imbalance among the foreground classes has an adverse effect on performance and that it can be alleviated at the batch level . Finally , we train Faster R-CNN using our pRoI generator and , compared to conventional training , obtain better or on-par performance for low IoUs and significant improvements when trained for higher IoUs for Pascal VOC and MS COCO datasets .
We propose a new feature fusion module in each network to explore complex nonlinear predictors and improve accuracy . Then , we propose a novel loss named semi-focal loss and we use off-line hard example mining to solve the problem of class imbalance and error labeling . Finally , we propose a new training skill of cutting patches in the whole slide image , considering the size and distribution of mitotic cells .
In this work , we propose to combine the residual network ( ResNet ) with the self-attention mechanism to achieve better performance in text-independent speaker recognition with fewer parameters and lower computational cost . In addition , the Cluster-Range Loss based on a well-designed online exemplar mining is proposed to directly shrink the intra-class variation and to enlarge the inter-class distance . Experiments on Voxceleb dataset are conducted to verify the effectiveness of the proposed scheme .
Second , a strategy of horizontal region generation is proposed to fully utilize the aligned property of widget arrangement , which generates all the region candidates in a horizontal line at once . Finally , a variant of online hard example mining is employed to alleviate the problem of imbalance samples , which explicitly restricts the ratio of foreground and background to achieve better balance . We conduct experiments on a proposed benchmark dataset .
In this report , we propose a robust deep face detection approach based on Faster R-CNN . In our approach , we exploit several new techniques including new multi-task loss function design , online hard example mining , and multi-scale training strategy to improve Faster R-CNN in multiple aspects . The proposed approach is well suited for face detection , so we call it Face R-CNN .
In this paper , we present a novel approach that learns a semantic connection between pedestrian head and other body parts for head detection . Specifically , the proposed model , named as HeadNet , is based on PVANet backbone and also introduces beneficial strategies including online hard example mining ( OHEM ) , fine-grained feature maps , RoI Align and Body in Context ( BiC ) . Experiments demonstrate that our approach is able to utilize spatial semantics of the entire body effectively , and gains inspiring performance for pedestrian head detection .
The two-stream network is jointly optimized by fusing the flow and RGB feature maps at different levels . Additionally , the training stage incorporates an online hard example mining strategy to address the extreme foreground-background imbalance typically observed in any detection pipeline . Instead of heuristically sampling the candidate segments for the final activity classification stage , we rank them according to their performance and only select the worst performers to update the model .
We show that our optimization method can be interpreted as a principled Hard Example Mining strategy . Similar to an online hard example mining strategy in essence and in practice , the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning . It only requires adding a softmax layer and maintaining a history of the loss values for each training example to compute adaptive sampling probabilities .
Considering the highly varying shape of atrium and the branchy structure of associated pulmonary veins , we propose to adopt a pyramid module to collect semantic cues in feature maps from multiple scales for fine-grained segmentation . Also , to promote our network in classifying the hard examples , we propose an Online Hard Negative Example Mining strategy to identify voxels in slices with low classification certainties and penalize the wrong predictions on them . Finally , we devise a competitive training scheme to further boost the generalization ability of networks .
To generate nodule candidates , we build a full 3D CNN model that employs 3D U-Net architecture as the backbone of a region proposal network ( RPN ) . We adopt multi-task residual learning and online hard negative example mining strategy to accelerate the training process and improve the accuracy of nodule detection . Then , a 3D DenseNet-based model is presented to reduce false positive nodules .
subsection : Hard example mining Hard example mining is an important strategy to improve model quality , and has been studied extensively in image classification and general object detection . The main idea is to find some hard positive and hard negative examples at each step , and put more effort into training on those hard examples .
We use a smaller threshold ( 0.3 ) because ( 1 ) this will allow imperfectly matched anchors to be able to localize the face , which may be useful during the testing and ( 2 ) the regression task has less supervision since unlike classification , there are no negative anchors for computing loss and the positive anchors are usually sparse . subsubsection : Anchor - level hard example mining OHEM has been proven to be useful for object detection and face detection in .
document : Training Region - based Object Detectors with Online Hard Example Mining The field of object detection has made significant advances riding on the wave of region - based ConvNets , but their training procedure still includes many heuristics and hyperparameters that are costly to tune .
Since these models do not use SVMs , and are trained purely online with SGD , existing hard example mining techniques can not be immediately applied . This work addresses that problem by introducing an online hard example mining algorithm that improves optimization and detection accuracy . We briefly review hard example mining , modern ConvNet - based object detection , and relationships to concurrent works using hard example selection for training deep networks . paragraph : Hard example mining . There are two hard example mining algorithms in common use . The first is used when optimizing SVMs .
subsection : Online hard example mining Recall the alternating steps that define a hard example mining algorithm : ( a ) for some period of time a fixed model is used to find new examples to add to the active training set ; ( b ) then , for some period of time the model is trained on the fixed active training set . In the context of SVM - based object detectors , such as the SVMs trained in R - CNN or SPPnet , step ( a ) inspects a variable number of images ( often 10 ’s or 100 ’s ) until the active training set reaches a threshold size , and then in step ( b ) the SVM is trained to convergence on the active training set .
Furthermore , there are many patterns similar to text strokes , such as fences , lattices , etc . Therefore , we adopt Online Hard Example Mining ( OHEM ) to during training to better distinguish these patterns .
Automatic selection of these hard examples can make training more effective and efficient . OHEM is a simple and intuitive algorithm that eliminates several heuristics and hyperparameters in common use . But more importantly , it yields consistent and significant boosts in detection performance on benchmarks like PASCAL VOC 2007 and 2012 .
A lot of methods have been proposed to improve the performance of DNNs , such as the ReLU ( Rectified Linear Unit ) which is a widely used alternative of the sigmoid activation function , Local Response Normalization and Batch Normalization . What 's more , some methods , OHEM ( Online Hard Sample Mining ) for instance , aims to augment the capability of generalization of DNNs in certain scenarios . In this paper , we choose the LeNet architecture as our baseline model , which is known to work well on digit classification tasks .
In this paper , we present the Stratified Online Hard Example Mining ( S-OHEM ) algorithm for training higher efficiency and accuracy detectors . S-OHEM exploits OHEM with stratified sampling , a widely-adopted sampling technique , to choose the training examples according to this influence during hard example mining , and thus enhance the performance of object detectors . We show through systematic experiments that S-OHEM yields an average precision ( AP ) improvement of 0.5 % on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7 . For KITTI 2012 , both results of the same metric are 1.6 % . Regarding the mean average precision ( mAP ) , a relative increase of 0.3 % and 0.5 % ( 1 % and 0.5 % ) is observed for VOC07 ( KITTI12 ) using the same set of IoU threshold . Also , S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors .
Thirdly , we present a multi-stage image-based online hard example mining ( MSI-OHEM ) framework specifically designed for single-stage detectors . MSI-OHEM performs fine-tuning on hard examples and the ones with slightly-insufficient IOU that are considered true positives . Compared to other classical object detectors , the proposed detector achieves very competitive result in terms of average precision ( AP ) and computational speed .
To address this issue , in this paper , we propose a robust face detector by putting more training focus on those hard images . This issue is most related to anchor - level hard example mining discussed in OHEM . However , due to the sparsity of ground - truth faces and positive anchors , traditional anchor - level hard example mining mainly focuses on mining hard negative anchors , and mining hard anchors on well - detected images exhibits less effectiveness since there is no useful information that can be further exploited in these easy images .
But these problems are not existed for two - stage detectors , owing to the proposal - driven mechanism . To handle the former imbalance problem , example mining based methods such as OHEM are in common use , but they directly abandon most examples and the training is inefficient . For the latter imbalance , the recent work , Focal Loss , has tried to address it by rectifying the cross - entropy loss function to a elaborately designed form .
Automatic water feature delineation from satellite imagery is a challenging task due to the variability of sensor parameters , acquisition conditions , and geographical heterogeneity of scenes . To mitigate above challenges , we proposed a fully automatic method for surface water feature dilatation from the moderate resolution satellite imagery through the integration of adaptive and automatic training sample collection by a modulated spectral reflectance curve and one-class classification algorithm . The specific modulation codes that are partially able to represent water features have been chosen to collect positive training samples for the target class .
By virtue of the ability to reveal the non-linear structure hidden in the high-dimensional image space , LPP is an efficient manifold learning method to analyze the local intrinsic features on the manifold of local facial areas . By searching out patches online in the LPP sub-space , which makes the resultant training set tailored to the testing patch , our algorithm performed the adaptive sample selection and then effectively restored the lost high-frequency components of the low-resolution face image by patch-based eigen transformation using the dynamic training set . Finally , experiments fully demonstrated that the proposed method can achieve good performance of super-resolution reconstruction by utilizing a relative small sample .
To perform interactive pattern analysis , we propose two online pattern classification methods , called interactive random forests ( IRF ) and adaptive random forests ( ARF ) , that adapt a composite classifier known as random forests for relevance feedback . IRF improves the efficiency of regular random forests ( RRF ) with a novel two-level resampling technique called biased random sample reduction , while ARF boosts the performance of RRF with two adaptive learning techniques called dynamic feature extraction and adaptive sample selection . During interactive multimedia retrieval , both ARF and IRF run two to three times faster than RRF while achieving comparable precision and recall against the latter .
This research proposes a systematic methodology to develop an effective online evolving PHM method with adaptive sampling mechanism against continuous stream data . An adaptive sample selection strategy is developed to effectively select the representative samples in both off-line and online environment . Meanwhile , a probabilistic theory-based modeling approach is developed to update the model with newly selected samples .
The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images . An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point . Thus , each patch can preserve the texture structure around its key point rather than all patches have a predetermined size .
Here a conflict arises : More data will increase the reliability of the prediction model but will also increase the costs and the time necessary for the variation study . This paper reports about the utilization of gaussian process based machine learning that can handle the described conflict by enabling an adaptive sampling of training data .
In order to improve the reconstruction accuracy and reduce the workload , the algorithm of compressive sensing based on the iterative threshold is combined with the method of adaptive selection of the training sample , and a new algorithm of adaptive compressive sensing is put forward . The three kinds of training sample are used to reconstruct the spectral reflectance of the testing sample based on the compressive sensing algorithm and adaptive compressive sensing algorithm , and the color difference and error are compared .
The simplified multi-objective optimisation model is then solved by the combination of a multi-objective evolutionary algorithm called $ { \rm \epsilon } $ e-NSGAII and extreme learning machine-based surrogate modelling with adaptive training data sampling . This combination significantly reduces the unaffordable computing burden .
A two-stage data association is first developed to handle the drifting targets stemming from occlusions and people 's abrupt motion changes . Subsequently , a novel online appearance learning is developed by using the incremental/decremental support vector machine with an adaptive training sample collection strategy to ensure reliable data association and rapid learning . Experimental results show the effectiveness and robustness of the proposed method while demonstrating its compatibility with real-time applications .
This shows that how to select positive and negative training samples is important for current object detectors . Then , we propose an Adaptive Training Sample Selection ( ATSS ) to automatically select positive and negative samples according to statistical characteristics of object . It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them .
Aircraft tractor supplier selection ( ATSS ) is critical for equipment procurement management in airport ground handling service ( AGHS ) companies because it guarantees the work safety of airport ramps . Although AGHS ATSS plays a crucial role in the safety of ground handling ramp operations , few studies have analyzed the ATSS of AGHS companies . This paper describes a new approach to compare the Delphi method , analytic hierarchy process , involvement cost-factor measures , and multi-choice goal programming by considering both financial and nonfinancial factors for ATSS from a financial perspective .
It allows the network to compute the correlation between each feature parallelly , and using shallower layers to extract more features , compared with the CNN-LSTM architecture . Experimental results show that our Atss-Net yields better performance than the VoiceFilter , although it only contains half of the parameters . Furthermore , our proposed model also demonstrates promising performance in speech enhancement .
ATS was the best design according to these measurements . We then compared ATS , adaptive cluster sampling ( ACS ) , adaptive two-stage sequential sampling ( ATSS ) , and the currently used stratified random sampling design . ATS performed better than the other two approaches and the current stratified random sampling design .
Additionally , this work copes with a novel optimization method combining multi-objective optimization with a grammatical evolution methodology . We implemented the ATS testing three different fitness functions under three mono-objective approaches and also two multi-objective ATSs . Experimental results test and compare them to the Buy and Hold strategy and a previous approach , beating both in returns and in number of positive operations .
The analysis was carried out descriptively to produce elements that support low energy criteria . Testing low energy elements using the Analytical Hierarchy Process ( AHP ) method to determine the weight of the priority elements of traditional low energy ATSS architectural design . The research results obtained are important factors in the ATSS design ( bale/ traditional house , berugak , and geleng/barn ) in Traditional Village Senaru , where low energy conceptors are site design factors ( orientation and land cover/pavement , geo-biological disturbances , vegetation , topography and type of land , and guardrail ) that are integrated in the green concept based on suitability and carrying capacity of the site and landscape .
As a building block for constructing ATSS , we also introduce the notion of accountable chameleon hash ( ACH ) , which is an extension of chameleon hash ( CH ) and might be of independent interest . We propose a concrete construction of ACH and show how to use it to construct an ATSS scheme .
A Domain-Specific Software Architecture ( DSSA ) could bring a large-scale reuse of software in the construction of application systems . Although there are several typical Avionics Testing Systems ( ATSs ) all over the world , the DSSA of ATS ( DSSAATS ) has never been put forward . ATS , as a software-intensive system , is such a system that can effectively be used to implement automatic , realtime , close-loop and non-invasive system testing for avionics .
The other example shows how the model set up enables strategies for dealing tool specific CD signature differences . In this case the balanced sampling enables a process control scheme which allows usage of the full tool park within the specified tight tolerance budget . Overall , this paper shows that the current rapid developments off the machine learning algorithms can be successfully used within the context of semiconductor manufacturing .
To fulfil all these constraints and to optimally use the available prior information , we propose a multi-stage self-weighted unequal probability sampling design . The design uses modern techniques such as : balanced sampling , spreading , stratified balancing , calibration , unequal probability sampling and power allocation . This sampling design meets the numerous requirements of this study and provides a very efficient estimator .
To mitigate the adverse effects caused thereby , we propose Libra R-CNN , a simple but effective framework towards balanced learning for object detection . It integrates three novel components : IoU-balanced sampling , balanced feature pyramid , and balanced L1 loss , respectively for reducing the imbalance at sample , feature , and objective level . Benefitted from the overall balanced design , Libra R-CNN significantly improves the detection performance .
The top accuracy of object detection to date is led by region-based approaches , where the per-region stage is responsible for recognizing proposals generated by the region proposal network . In that stage , sampling heuristics ( e.g. , OHEM , IoU-balanced sampling ) is always applied to select a part of examples during training . But nowadays , existing samplers ignore the overlaps among examples , which may result in some low-quality predictions preserved .
Our implementation was done in Torch . We adopt the balanced sampling from , by sampling from context dependent state with probability . We keep throughout the experiments during cross - entropy training .
On the convergence of the Laplace approximation and noise-level-robustness of Laplace-based Monte Carlo methods for Bayesian inverse problems Improved particle filter track-before-detection method for weak target detection A MULTI-SCAN MIXTURE PARTICLE FILTER FOR JOINT DETECTION AND TRACKING OF MULTIPLE TARGETS Geostatistical characterization of local accuracies in remotely sensed land cover change categorization with complexly configured reference samples A label-free fiber optic biosensor for Salmonella Typhimurium detection Short-term forecasting of non-stationary time series using multiple feature selection methods Fuzzy Clustering Ensemble for Prioritized Sampling Based on Average and Rough Patterns Flexible Robotic Grasping with Sim-to-Real Transfer based Reinforcement Learning Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation The Reactor: A Sample-Efficient Actor-Critic Architecture An Effective Approach to Unsupervised Machine Translation DeepMatching: Hierarchical Deformable Dense Matching Invertible Conditional GANs for image editing Image Super-Resolution via Dual-State Recurrent Networks Training deep convolution neural network with hard example mining for airport detection Tumor Tissue Segmentation for Histopathological Images Deep Strip-Based Network with Cascade Learning for Scene Text Localization A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining Generating Positive Bounding Boxes for Balanced Training of Object Detectors CHS-NET: A Cascaded Neural Network with Semi-Focal Loss for Mitosis Detection Self-attention based speaker recognition using Cluster-Range Loss Widget Detection Network: widget detection in mobile screenshot with region-based attention networks Face R-CNN HeadNet: Pedestrian Head Detection Utilizing Body in Context Two-Stream Region Convolutional 3D Network for Temporal Activity Detection SGD with Hardness Weighted Sampling for Distributionally Robust Deep Learning Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation Simultaneous Accurate Detection of Pulmonary Nodules and False Positive Reduction Using 3D CNNs Robust Face Detection via Learning Small Faces on Hard Images Training Region-Based Object Detectors with Online Hard Example Mining Shape Robust Text Detection With Progressive Scale Expansion Network Training Region-Based Object Detectors with Online Hard Example Mining Improving the lenet with batch normalization and online hard example mining for digits recognition S-OHEM: Stratified Online Hard Example Mining for Object Detection Real-Time Single-Stage Vehicle Detector Optimized by Multi-Stage Image-Based Online Hard Example Mining Robust Face Detection via Learning Small Faces on Hard Images Gradient Harmonized Single-stage Detector A Fully Automatic Water Feature Extraction Method with Adaptive Training Samples and One-Class Classification Algorithms An adaptive learning method for face hallucination using Locality Preserving Projections Interactive pattern analysis for relevance feedback in multimedia information retrieval Methodology of Adaptive Prognostics and Health Management Using Streaming Data in Big Data Environment A Novel Neural Network for Remote Sensing Image Matching Der Einsatz von Gaußprozessen zur Beschleunigung der automatischen Wissensakquisition The study of key technology on spectral reflectance reconstruction based on the algorithm of adaptive compressive sensing Multi-objective robust dynamic VAR planning in power transmission girds for improving short-term voltage stability under uncertainties Online multi-person tracking with two-stage data association and online appearance model learning Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection A COMPARISON OF MODELS USED FOR AIRCRAFT TRACTOR SUPPLIER SELECTION FROM A FINANCIAL PERSPECTIVE Atss-Net: Target Speaker Separation via Attention-based Neural Network Performance comparison of traditional sampling designs and adaptive sampling designs for fishery-independent surveys: A simulation study A hybrid automated trading system based on multi-objective grammatical evolution Low energy site in the Sasak Senaru Traditional Architecture Accountable Trapdoor Sanitizable Signatures The domain-specific software architecture of avionics testing system Splendidly blended: a machine learning set up for CDU control Complex national sampling design for long-term monitoring of protected dry grasslands in Switzerland Libra R-CNN: Towards Balanced Learning for Object Detection Overlap Sampler for Region-Based Object Detection The IBM 2016 English Conversational Telephone Speech Recognition System
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IEA/AIE ArXiv ACL International Journal of Computer Vision ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition IGARSS MMAsia ICDAR ACML FG STACOM@MICCAI ICASSP 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) AAAI FG ISPEC WACV INTERSPEECH
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