However , the commonly used loss function softmax loss and highly efficient network architectures for common visual tasks are not as effective for face recognition . In this paper , we propose a novel loss function named Li-ArcFace based on ArcFace . Li-ArcFace takes the value of the angle through a linear function as the target logit rather than through cosine function , which has better convergence and performance on low dimensional embedding feature learning for face recognition . Recently , an automated gradient-based optimization procedure was published by the authors based on the minimization of a loss function between simulated and experimental physical properties . The applicability of gradient-based procedures is not trivial at all because of two reasons : firstly , simulation data are affected by statistical noise , and secondly , the molecular simulations required for the loss function evaluations are extremely time-consuming . Within the optimization process , gradients and Hessians were approximated by finite differences so that additional simulations for the respective modified parameter sets were required . M corresponds to finding optimal weights θ k for all the metafeatures for all events in such a way that the average loss over all events between the target vector t and the prediction vector y is minimized , according to some loss function L. The most natural choice of loss function is one that is based on the multinomial distribution . Specifically , it trains a loss function to distinguish between real and fake samples by designated margins , while learning a generator alternately to produce realistic samples by minimizing their losses . The LS - GAN further regularizes its loss function with a Lipschitz regularity condition on the density of real data , yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN . We will further present a Generalized LS - GAN ( GLS - GAN ) and show it contains a large family of regularized GAN models , including both LS - GAN and Wasserstein GAN , as its special cases . We assume that the input image and domain label pair is given by the training data . On the other hand , the loss function for the domain classification of fake images is defined as In other words , tries to minimize this objective to generate images that can be classified as the target domain . Reconstruction Loss . The loss term of stage 3 exhibits the following form : Here is the network outputs of this stage , representing a list of category predictions for all instances : . This loss term is dependent on and ( where is used for generating the RoI feature ) . section : End - to - End Training We present deep convolutional neural network-based methods for automatic matching of tattoo images based on the AlexNet and Siamese networks . Furthermore , we show that rather than using a simple contrastive loss function , triplet loss function can significantly improve the performance of a tattoo matching system . Extensive experiments on a recently introduced Tatt-C dataset show that our method is able to capture the meaningful structure of tattoos and performs significantly better than many competitive tattoo recognition algorithms . A compact descriptor named QuadrupletNet is generated by combining the proposed quadruplet loss and L2Net CNN architecture . From our experiment , QuadrupletNet shows better performance on the Brown dataset and Hpatches dataset than Triplet loss methods on the same training set . The pre-trained QuadrupletNet is publicly available . Here , we optimize a speaker embedding model with prototypical network loss ( PNL ) , a state-of-the-art approach for the few-shot image classification task . The resulting embedding model outperforms the state-of-the-art triplet loss based models in both speaker verification and identification tasks , for both seen and unseen speakers . ( 2 ) We apply attention mechanism to select effective features from food images incorporating with recipe embeddings , and adopt an adversarial learning strategy to enhance modality alignment . ( 3 ) We design a new triplet loss scheme with an effective sampling strategy to reduce the noise impact on retrieval results . The experimental results show that our framework clearly outperforms the state-of-art methods in terms of median rank and recall rate at top k on the Recipe 1 M dataset . In this paper , we investigate an open research task of cross-modal retrieval between cooking recipes and food images , and propose a novel framework Adversarial Cross-Modal Embedding ( ACME ) to resolve the cross-modal retrieval task in food domains . Specifically , the goal is to learn a common embedding feature space between the two modalities , in which our approach consists of several novel ideas : ( i ) learning by using a new triplet loss scheme together with an effective sampling strategy , ( ii ) imposing modality alignment using an adversarial learning strategy , and ( iii ) imposing cross-modal translation consistency such that the embedding of one modality is able to recover some important information of corresponding instances in the other modality . ACME achieves the state-of-the-art performance on the benchmark Recipe1 M dataset , validating the efficacy of the proposed technique . We use Convolutional Neural Networks ( CNN ) to learn Patch2Vec . In particular , we train a CNN on labeled images with a triplet‐loss objective function . The trained network encodes a given patch to a 128D vector . We improve upon this technique by computing a distance function between two utterances which takes into account common phonetic units . The whole network is optimized by employing a triplet-loss objective function . The proposed approaches are evaluated on commonly used datasets such as NIST SRE 2010 and RSR2015 . For apparel classification , we pass the feature embeddings through a softmax classifier , while , the in-shop and cross-domain retrieval pipelines use a triplet-loss based optimization approach , such that squared Euclidean distance between embeddings measures the dissimilarity between the images . Unlike previous works that relied on bounding box , key clothing landmarks or human joint detectors to assist the final deep classifier , proposed framework can be trained directly on the provided category labels or generated triplets for triplet loss optimization . Lastly , Experimental results on the DeepFashion fine-grained categorization , and in-shop and consumer-to-shop retrieval datasets provide a comparative analysis with previous work performed in the domain . In this paper , an efficient CNN architecture is proposed for accurate still-to-video FR from a single reference still . The CCM-CNN is based on new cross-correlation matching ( CCM ) and triplet-loss optimization methods that provide discriminant face representations . The matching pipeline exploits a matrix Hadamard product followed by a fully connected layer inspired by adaptive weighted cross-correlation . Inspired by previous works , we propose to learn a modality-invariant image-text embedding for image-sentence matching by involving adversarial learning . On top of the triplet loss -- based baseline , we design a modality classification network with an adversarial loss , which classifies an embedding into either the image or text modality . In addition , the multi-stage training procedure is carefully designed so that the proposed network not only imposes the image-text similarity constraints by ground-truth labels , but also enforces the image and text embedding distributions to be similar by adversarial learning . This approach works by processing individually the speech and the image signal . In the speech domain , speaker diarization is performed using identity embeddings created by a triplet loss DNN that uses i-vectors as input . The triplet DNN is trained with an additional regularization loss that minimizes the variance of both positive and negative distances . One is learning the part-based local features to form more informative feature descriptors . The other is designing effective metric learning loss functions such as the triplet loss family . We argue that learning global features with classification loss could achieve the same goal , even with some simple and cost-effective architecture design . TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks , meant to project speech sequences into a fixed-dimensional euclidean space . Thanks to the triplet loss paradigm used for training , the resulting sequence embeddings can be compared directly with the euclidean distance , for speaker comparison purposes . Experiments on short ( between 500ms and 5s ) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks . For a given support set and sample to classify , it is enough for to be sufficiently aligned with pairs such that and misaligned with the rest . This kind of loss is also related to methods such as Neighborhood Component Analysis ( NCA ) , triplet loss or large margin nearest neighbor . However , the objective that we are trying to optimize is precisely aligned with multi - way , one - shot classification , and thus we expect it to perform better than its counterparts . Some recent work has reduced this dimensionality using PCA , but this is a linear transformation that can be easily learnt in one layer of the network . In contrast to these approaches , FaceNet directly trains its output to be a compact 128 - D embedding using a triplet - based loss function based on LMNN . Our triplets consist of two matching face thumbnails and a non - matching face thumbnail and the loss aims to separate the positive pair from the negative by a distance margin . Although we did not directly compare to other losses , the one using pairs of positives and negatives , as used in Eq . ( 2 ) , we believe that the triplet loss is more suitable for face verification . The motivation is that the loss from encourages all faces of one identity to be projected onto a single point in the embedding space . Extensive evaluation of template adaptation on the IJB - A dataset has generated surprising results . First , template adaptation outperforms all top performing techniques in the literature : convolutional networks combined with triplet loss similarity , joint Bayesian metric learning , pose specialized networks , 2D alignment , 3D frontalization and novel convolutional network architectures . Second , template adaptation when combined with these other techniques results in nearly equivalent performance . VGG - Face is the application of the VGG - 16 convolutional network architecture trained on a newly curated dataset of 2.6 M images of 2622 subjects . This representation includes triplet loss embedding and 2D alignment for normalization to provide state of the art performance . FaceNet applied the inception CNN architecture to the problem of face verification . In recent years , several unsupervised , " contrastive " learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks . We show that this family of algorithms maximizes a lower bound on the mutual information between two or more " views " of an image ; typical views come from a composition of image augmentations . Unsupervised contrastive learning has gained increasing attention in the latest research and has proven to be a powerful method for learning representations from unlabeled data . However , little theoretical analysis was known for this framework . In this paper , we study the optimization of deep unsupervised contrastive learning . We prove that , by applying end-to-end training that simultaneously updates two deep over-parameterized neural networks , one can find an approximate stationary solution for the non-convex contrastive loss . Contrastive unsupervised learning has recently shown encouraging progress , e.g. , in Momentum Contrast ( MoCo ) and SimCLR . In this note , we verify the effectiveness of two of SimCLR 's design improvements by implementing them in the MoCo framework . From a perspective on contrastive learning as dictionary look-up , we build a dynamic dictionary with a queue and a moving-averaged encoder . This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning . MoCo provides competitive results under the common linear protocol on ImageNet classification . In this work , we extend our classification model 's task loss with an unsupervised auxiliary loss on the word-embedding level of the model . This is to ensure that the learned word representations contain both task-specific features , learned from the supervised loss component , and more general features learned from the unsupervised loss component . We evaluate our approach on the task of temporal relation extraction , in particular , narrative containment relation extraction from clinical records , and show that continued training of the embeddings on the unsupervised objective together with the task objective gives better task-specific embeddings , and results in an improvement over the state of the art on the THYME dataset , using only a general-domain part-of-speech tagger as linguistic resource . Such a consistency constraint is necessary but not sufficient to predict accurate poses . We therefore complement it with a supervised loss aiming to predict the correct pose in a small set of labeled images , and with a regularization term that penalizes drift from initial predictions . Furthermore , we propose a method to estimate camera pose jointly with human pose , which lets us utilize multiview footage where calibration is difficult , e.g. , for pan-tilt or moving handheld cameras . ( 2 ) 3D CNN and LSTM used in the proposed deep learning framework yields better results than that of the 3D deep feature concatenation for MVI prediction . ( 3 ) Deeply supervised loss function that makes full use of the spatially and temporally discriminative features from Contrast-enhanced MR can further improve the performance of MVI prediction . The quadruplet ranking loss can model the semantically similar and dissimilar constraints to preserve cross-modal relative similarity ranking information . The semi-supervised contrastive loss is able to maximize the semantic similarity on both labeled and unlabeled data . Compared to the existing methods , CDMLMR exploits not only the similarity ranking information but also unlabeled cross-modal data , and thus boosts cross-modal retrieval accuracy . The loss also shows clear benefits for robustness to natural corruptions on standard benchmarks on both calibration and accuracy . Compared to cross entropy , our supervised contrastive loss is more stable to hyperparameter settings such as optimizers or data augmentations . In this paper we investigate how these two approaches ( supervised & self-supervised ) can be effectively combined , so that a depth model can learn to encode true scale from sparse supervision while achieving high fidelity local accuracy by leveraging geometric cues . To this end , we propose a novel supervised loss term that complements the widely used photometric loss , and show how it can be used to train robust semi-supervised monocular depth estimation models . Furthermore , we evaluate how much supervision is actually necessary to train accurate scale-aware monocular depth models , showing that with our proposed framework , very sparse LiDAR information , with as few as 4 beams ( less than 100 valid depth values per image ) , is enough to achieve results competitive with the current state-of-the-art . Components also differ in their scale , as some focus on fine-resolution patterns while others on coarser , more global structure . A supervised version of the contrastive divergence algorithm is applied to learn these features from labeled image data . We demonstrate performance on two real-world image databases and compare it to a classifier and a Markov random field . The more and the large size of the pattern being trained , the longer the time it takes the network . LVQ is a method of training the unsupervised competitive layer will automatically learn to classify input vectors into certain classes . The classes are generated depends on the distance between the input vectors . If this is the case then the source classification model , , can be directly applied to the target representations , elimating the need to learn a separate target classifier and instead setting , . The source classification model is then trained using the standard supervised loss below : We are now able to describe our full general framework view of adversarial adaptation approaches . We note that all approaches minimize source and target representation distances through alternating minimization between two functions . observations x from q i , and the task loss is represented by the error between the model 's output for x and the corresponding target values y for that observation and task . Two common loss functions used for supervised classification and regression are cross - entropy and mean - squared error ( MSE ) , which we will describe below ; though , other supervised loss functions may be used as well . For regression tasks using mean - squared error , the loss takes the form : In the beginning the total loss and the learning gradients are thus dominated by the supervised loss component , i.e. , the labeled data only . We have found it to be very important that the ramp - up of the unsupervised loss component is slow enough - otherwise , the network gets easily stuck in a degenerate solution where no meaningful classification of the data is obtained . Our approach is somewhat similar to the Γ - model of the ladder network by [ reference ] , but conceptually simpler . Figure 2 shows the classification error graphs for standard supervised training and temporal ensembling . Clearly our methods provide considerable resistance to wrong labels , and we believe this is because the unsupervised loss term encourages the mapping function implemented by the network to be flat in the vicinity of all input data points , whereas the supervised loss term enforces the mapping function to have a specific value in the vicinity of the labeled input data points . This means that even the wrongly labeled inputs play a role in shaping the mapping function - the unsupervised loss term smooths the mapping function and thus also the decision boundaries , effectively fusing the inputs into coherent clusters , whereas the excess of correct labels in each class is sufficient for locking the clusters to the right output vectors through the supervised loss term . In this study , we propose a triplet-loss embedded deep regressor network to predict changes of BP using expressive prosodic features for on-boarding emergency room patients between pre- and post-triage sessions . The framework achieves correlations of 0.419 and 0.386 in predicting changes in SBP ( systolic blood pressure ) and DBP ( diastolic blood pressure ) respectively , which is 26.1 % and 17.3 % relative improvement compared to DNN-regressors without triplet-loss embedding . Further correlation analyses on the relationship between prosodic features and BP changes are presented . Inspired by the triplet loss [ 34 ] function , we propose a generalization that : 1 ) defines a metric that considers the number of agreeing labels between pairs of elements ; and 2 ) disregards the notion of anchor , replacing d(A1 , A2 ) < d(A1 , B ) by d(A , B ) < d(C , D ) , for A , B , C , D distance constraints , according to the number of agreeing labels between pairs . As the triplet loss formulation , our proposal also privileges small distances between positive pairs , but at the same time explicitly enforces that the distance between other pairs corresponds directly to their similarity in terms of agreeing labels . This yields feature embeddings with a strong correspondence between the classes centroids and their semantic descriptions , i.e. , where elements are closer to others that share some of their labels than to elements with fully disjoint labels membership . To avoid this side effect , we apply batch normalization to the architecture instead of face alignment and feed the network with original images . Moreover , considering the tiny differences between classes caused by the same facial movements , a triplet-loss learning method is used to train the architecture , which improves the discrimination of deep features . Experiments show that the proposed architecture achieves superior performance to other state-of-the-art methods on the FER2013 dataset . The second term is the - constancy term , which requires that is invariant under . In practice , we have experimented with multiple forms of including Mean Squared Error ( MSE ) and cosine distance , as well as other variants including metric learning losses ( hinge ) and triplet losses . The performance is mostly unchanged , and we report results using the simplest MSE solution .
However , the commonly used loss function softmax loss and highly efficient network architectures for common visual tasks are not as effective for face recognition . In this paper , we propose a novel loss function named Li-ArcFace based on ArcFace . Li-ArcFace takes the value of the angle through a linear function as the target logit rather than through cosine function , which has better convergence and performance on low dimensional embedding feature learning for face recognition .
Recently , an automated gradient-based optimization procedure was published by the authors based on the minimization of a loss function between simulated and experimental physical properties . The applicability of gradient-based procedures is not trivial at all because of two reasons : firstly , simulation data are affected by statistical noise , and secondly , the molecular simulations required for the loss function evaluations are extremely time-consuming . Within the optimization process , gradients and Hessians were approximated by finite differences so that additional simulations for the respective modified parameter sets were required .
M corresponds to finding optimal weights θ k for all the metafeatures for all events in such a way that the average loss over all events between the target vector t and the prediction vector y is minimized , according to some loss function L. The most natural choice of loss function is one that is based on the multinomial distribution .
Specifically , it trains a loss function to distinguish between real and fake samples by designated margins , while learning a generator alternately to produce realistic samples by minimizing their losses . The LS - GAN further regularizes its loss function with a Lipschitz regularity condition on the density of real data , yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN . We will further present a Generalized LS - GAN ( GLS - GAN ) and show it contains a large family of regularized GAN models , including both LS - GAN and Wasserstein GAN , as its special cases .
We assume that the input image and domain label pair is given by the training data . On the other hand , the loss function for the domain classification of fake images is defined as In other words , tries to minimize this objective to generate images that can be classified as the target domain . Reconstruction Loss .
The loss term of stage 3 exhibits the following form : Here is the network outputs of this stage , representing a list of category predictions for all instances : . This loss term is dependent on and ( where is used for generating the RoI feature ) . section : End - to - End Training
We present deep convolutional neural network-based methods for automatic matching of tattoo images based on the AlexNet and Siamese networks . Furthermore , we show that rather than using a simple contrastive loss function , triplet loss function can significantly improve the performance of a tattoo matching system . Extensive experiments on a recently introduced Tatt-C dataset show that our method is able to capture the meaningful structure of tattoos and performs significantly better than many competitive tattoo recognition algorithms .
A compact descriptor named QuadrupletNet is generated by combining the proposed quadruplet loss and L2Net CNN architecture . From our experiment , QuadrupletNet shows better performance on the Brown dataset and Hpatches dataset than Triplet loss methods on the same training set . The pre-trained QuadrupletNet is publicly available .
Here , we optimize a speaker embedding model with prototypical network loss ( PNL ) , a state-of-the-art approach for the few-shot image classification task . The resulting embedding model outperforms the state-of-the-art triplet loss based models in both speaker verification and identification tasks , for both seen and unseen speakers .
( 2 ) We apply attention mechanism to select effective features from food images incorporating with recipe embeddings , and adopt an adversarial learning strategy to enhance modality alignment . ( 3 ) We design a new triplet loss scheme with an effective sampling strategy to reduce the noise impact on retrieval results . The experimental results show that our framework clearly outperforms the state-of-art methods in terms of median rank and recall rate at top k on the Recipe 1 M dataset .
In this paper , we investigate an open research task of cross-modal retrieval between cooking recipes and food images , and propose a novel framework Adversarial Cross-Modal Embedding ( ACME ) to resolve the cross-modal retrieval task in food domains . Specifically , the goal is to learn a common embedding feature space between the two modalities , in which our approach consists of several novel ideas : ( i ) learning by using a new triplet loss scheme together with an effective sampling strategy , ( ii ) imposing modality alignment using an adversarial learning strategy , and ( iii ) imposing cross-modal translation consistency such that the embedding of one modality is able to recover some important information of corresponding instances in the other modality . ACME achieves the state-of-the-art performance on the benchmark Recipe1 M dataset , validating the efficacy of the proposed technique .
We use Convolutional Neural Networks ( CNN ) to learn Patch2Vec . In particular , we train a CNN on labeled images with a triplet‐loss objective function . The trained network encodes a given patch to a 128D vector .
We improve upon this technique by computing a distance function between two utterances which takes into account common phonetic units . The whole network is optimized by employing a triplet-loss objective function . The proposed approaches are evaluated on commonly used datasets such as NIST SRE 2010 and RSR2015 .
For apparel classification , we pass the feature embeddings through a softmax classifier , while , the in-shop and cross-domain retrieval pipelines use a triplet-loss based optimization approach , such that squared Euclidean distance between embeddings measures the dissimilarity between the images . Unlike previous works that relied on bounding box , key clothing landmarks or human joint detectors to assist the final deep classifier , proposed framework can be trained directly on the provided category labels or generated triplets for triplet loss optimization . Lastly , Experimental results on the DeepFashion fine-grained categorization , and in-shop and consumer-to-shop retrieval datasets provide a comparative analysis with previous work performed in the domain .
In this paper , an efficient CNN architecture is proposed for accurate still-to-video FR from a single reference still . The CCM-CNN is based on new cross-correlation matching ( CCM ) and triplet-loss optimization methods that provide discriminant face representations . The matching pipeline exploits a matrix Hadamard product followed by a fully connected layer inspired by adaptive weighted cross-correlation .
Inspired by previous works , we propose to learn a modality-invariant image-text embedding for image-sentence matching by involving adversarial learning . On top of the triplet loss -- based baseline , we design a modality classification network with an adversarial loss , which classifies an embedding into either the image or text modality . In addition , the multi-stage training procedure is carefully designed so that the proposed network not only imposes the image-text similarity constraints by ground-truth labels , but also enforces the image and text embedding distributions to be similar by adversarial learning .
This approach works by processing individually the speech and the image signal . In the speech domain , speaker diarization is performed using identity embeddings created by a triplet loss DNN that uses i-vectors as input . The triplet DNN is trained with an additional regularization loss that minimizes the variance of both positive and negative distances .
One is learning the part-based local features to form more informative feature descriptors . The other is designing effective metric learning loss functions such as the triplet loss family . We argue that learning global features with classification loss could achieve the same goal , even with some simple and cost-effective architecture design .
TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks , meant to project speech sequences into a fixed-dimensional euclidean space . Thanks to the triplet loss paradigm used for training , the resulting sequence embeddings can be compared directly with the euclidean distance , for speaker comparison purposes . Experiments on short ( between 500ms and 5s ) speech turn comparison and speaker change detection show that TristouNet brings significant improvements over the current state-of-the-art techniques for both tasks .
For a given support set and sample to classify , it is enough for to be sufficiently aligned with pairs such that and misaligned with the rest . This kind of loss is also related to methods such as Neighborhood Component Analysis ( NCA ) , triplet loss or large margin nearest neighbor . However , the objective that we are trying to optimize is precisely aligned with multi - way , one - shot classification , and thus we expect it to perform better than its counterparts .
Some recent work has reduced this dimensionality using PCA , but this is a linear transformation that can be easily learnt in one layer of the network . In contrast to these approaches , FaceNet directly trains its output to be a compact 128 - D embedding using a triplet - based loss function based on LMNN . Our triplets consist of two matching face thumbnails and a non - matching face thumbnail and the loss aims to separate the positive pair from the negative by a distance margin .
Although we did not directly compare to other losses , the one using pairs of positives and negatives , as used in Eq . ( 2 ) , we believe that the triplet loss is more suitable for face verification . The motivation is that the loss from encourages all faces of one identity to be projected onto a single point in the embedding space .
Extensive evaluation of template adaptation on the IJB - A dataset has generated surprising results . First , template adaptation outperforms all top performing techniques in the literature : convolutional networks combined with triplet loss similarity , joint Bayesian metric learning , pose specialized networks , 2D alignment , 3D frontalization and novel convolutional network architectures . Second , template adaptation when combined with these other techniques results in nearly equivalent performance .
VGG - Face is the application of the VGG - 16 convolutional network architecture trained on a newly curated dataset of 2.6 M images of 2622 subjects . This representation includes triplet loss embedding and 2D alignment for normalization to provide state of the art performance . FaceNet applied the inception CNN architecture to the problem of face verification .
In recent years , several unsupervised , " contrastive " learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks . We show that this family of algorithms maximizes a lower bound on the mutual information between two or more " views " of an image ; typical views come from a composition of image augmentations .
Unsupervised contrastive learning has gained increasing attention in the latest research and has proven to be a powerful method for learning representations from unlabeled data . However , little theoretical analysis was known for this framework . In this paper , we study the optimization of deep unsupervised contrastive learning . We prove that , by applying end-to-end training that simultaneously updates two deep over-parameterized neural networks , one can find an approximate stationary solution for the non-convex contrastive loss .
Contrastive unsupervised learning has recently shown encouraging progress , e.g. , in Momentum Contrast ( MoCo ) and SimCLR . In this note , we verify the effectiveness of two of SimCLR 's design improvements by implementing them in the MoCo framework .
From a perspective on contrastive learning as dictionary look-up , we build a dynamic dictionary with a queue and a moving-averaged encoder . This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning . MoCo provides competitive results under the common linear protocol on ImageNet classification .
In this work , we extend our classification model 's task loss with an unsupervised auxiliary loss on the word-embedding level of the model . This is to ensure that the learned word representations contain both task-specific features , learned from the supervised loss component , and more general features learned from the unsupervised loss component . We evaluate our approach on the task of temporal relation extraction , in particular , narrative containment relation extraction from clinical records , and show that continued training of the embeddings on the unsupervised objective together with the task objective gives better task-specific embeddings , and results in an improvement over the state of the art on the THYME dataset , using only a general-domain part-of-speech tagger as linguistic resource .
Such a consistency constraint is necessary but not sufficient to predict accurate poses . We therefore complement it with a supervised loss aiming to predict the correct pose in a small set of labeled images , and with a regularization term that penalizes drift from initial predictions . Furthermore , we propose a method to estimate camera pose jointly with human pose , which lets us utilize multiview footage where calibration is difficult , e.g. , for pan-tilt or moving handheld cameras .
( 2 ) 3D CNN and LSTM used in the proposed deep learning framework yields better results than that of the 3D deep feature concatenation for MVI prediction . ( 3 ) Deeply supervised loss function that makes full use of the spatially and temporally discriminative features from Contrast-enhanced MR can further improve the performance of MVI prediction .
The quadruplet ranking loss can model the semantically similar and dissimilar constraints to preserve cross-modal relative similarity ranking information . The semi-supervised contrastive loss is able to maximize the semantic similarity on both labeled and unlabeled data . Compared to the existing methods , CDMLMR exploits not only the similarity ranking information but also unlabeled cross-modal data , and thus boosts cross-modal retrieval accuracy .
The loss also shows clear benefits for robustness to natural corruptions on standard benchmarks on both calibration and accuracy . Compared to cross entropy , our supervised contrastive loss is more stable to hyperparameter settings such as optimizers or data augmentations .
In this paper we investigate how these two approaches ( supervised & self-supervised ) can be effectively combined , so that a depth model can learn to encode true scale from sparse supervision while achieving high fidelity local accuracy by leveraging geometric cues . To this end , we propose a novel supervised loss term that complements the widely used photometric loss , and show how it can be used to train robust semi-supervised monocular depth estimation models . Furthermore , we evaluate how much supervision is actually necessary to train accurate scale-aware monocular depth models , showing that with our proposed framework , very sparse LiDAR information , with as few as 4 beams ( less than 100 valid depth values per image ) , is enough to achieve results competitive with the current state-of-the-art .
Components also differ in their scale , as some focus on fine-resolution patterns while others on coarser , more global structure . A supervised version of the contrastive divergence algorithm is applied to learn these features from labeled image data . We demonstrate performance on two real-world image databases and compare it to a classifier and a Markov random field .
The more and the large size of the pattern being trained , the longer the time it takes the network . LVQ is a method of training the unsupervised competitive layer will automatically learn to classify input vectors into certain classes . The classes are generated depends on the distance between the input vectors .
If this is the case then the source classification model , , can be directly applied to the target representations , elimating the need to learn a separate target classifier and instead setting , . The source classification model is then trained using the standard supervised loss below : We are now able to describe our full general framework view of adversarial adaptation approaches . We note that all approaches minimize source and target representation distances through alternating minimization between two functions .
observations x from q i , and the task loss is represented by the error between the model 's output for x and the corresponding target values y for that observation and task . Two common loss functions used for supervised classification and regression are cross - entropy and mean - squared error ( MSE ) , which we will describe below ; though , other supervised loss functions may be used as well . For regression tasks using mean - squared error , the loss takes the form :
In the beginning the total loss and the learning gradients are thus dominated by the supervised loss component , i.e. , the labeled data only . We have found it to be very important that the ramp - up of the unsupervised loss component is slow enough - otherwise , the network gets easily stuck in a degenerate solution where no meaningful classification of the data is obtained . Our approach is somewhat similar to the Γ - model of the ladder network by [ reference ] , but conceptually simpler .
Figure 2 shows the classification error graphs for standard supervised training and temporal ensembling . Clearly our methods provide considerable resistance to wrong labels , and we believe this is because the unsupervised loss term encourages the mapping function implemented by the network to be flat in the vicinity of all input data points , whereas the supervised loss term enforces the mapping function to have a specific value in the vicinity of the labeled input data points . This means that even the wrongly labeled inputs play a role in shaping the mapping function - the unsupervised loss term smooths the mapping function and thus also the decision boundaries , effectively fusing the inputs into coherent clusters , whereas the excess of correct labels in each class is sufficient for locking the clusters to the right output vectors through the supervised loss term .
In this study , we propose a triplet-loss embedded deep regressor network to predict changes of BP using expressive prosodic features for on-boarding emergency room patients between pre- and post-triage sessions . The framework achieves correlations of 0.419 and 0.386 in predicting changes in SBP ( systolic blood pressure ) and DBP ( diastolic blood pressure ) respectively , which is 26.1 % and 17.3 % relative improvement compared to DNN-regressors without triplet-loss embedding . Further correlation analyses on the relationship between prosodic features and BP changes are presented .
Inspired by the triplet loss [ 34 ] function , we propose a generalization that : 1 ) defines a metric that considers the number of agreeing labels between pairs of elements ; and 2 ) disregards the notion of anchor , replacing d(A1 , A2 ) < d(A1 , B ) by d(A , B ) < d(C , D ) , for A , B , C , D distance constraints , according to the number of agreeing labels between pairs . As the triplet loss formulation , our proposal also privileges small distances between positive pairs , but at the same time explicitly enforces that the distance between other pairs corresponds directly to their similarity in terms of agreeing labels . This yields feature embeddings with a strong correspondence between the classes centroids and their semantic descriptions , i.e. , where elements are closer to others that share some of their labels than to elements with fully disjoint labels membership .
To avoid this side effect , we apply batch normalization to the architecture instead of face alignment and feed the network with original images . Moreover , considering the tiny differences between classes caused by the same facial movements , a triplet-loss learning method is used to train the architecture , which improves the discrimination of deep features . Experiments show that the proposed architecture achieves superior performance to other state-of-the-art methods on the FER2013 dataset .
The second term is the - constancy term , which requires that is invariant under . In practice , we have experimented with multiple forms of including Mean Squared Error ( MSE ) and cosine distance , as well as other variants including metric learning losses ( hinge ) and triplet losses . The performance is mostly unchanged , and we report results using the simplest MSE solution .
AirFace:Lightweight and Efficient Model for Face Recognition Utilization of efficient gradient and Hessian computations in the force field optimization process of molecular simulations Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation Instance-Aware Semantic Segmentation via Multi-task Network Cascades Deep Tattoo Recognition A Deep Quadruplet Network for Local Descriptor Learning Centroid-based Deep Metric Learning for Speaker Recognition Sentence-based and Noise-robust Cross-modal Retrieval on Cooking Recipes and Food Images Learning Cross-Modal Embeddings With Adversarial Networks for Cooking Recipes and Food Images Patch2Vec: Globally Consistent Image Patch Representation Phonetic aware techniques for Speaker Verification Fine-grained Apparel Classification and Retrieval without rich annotations CNNs with cross-correlation matching for face recognition in video surveillance using a single training sample per person Modality-Invariant Image-Text Embedding for Image-Sentence Matching UPC Multimodal Speaker Diarization System for the 2018 Albayzin Challenge In Defense of the Classification Loss for Person Re-Identification TristouNet: Triplet loss for speaker turn embedding Matching Networks for One Shot Learning FaceNet: A unified embedding for face recognition and clustering Template adaptation for face verification and identification On Mutual Information in Contrastive Learning for Visual Representations Convergence of End-to-End Training in Deep Unsupervised Contrasitive Learning Improved Baselines with Momentum Contrastive Learning Momentum Contrast for Unsupervised Visual Representation Learning Word-Level Loss Extensions for Neural Temporal Relation Classification Learning Monocular 3D Human Pose Estimation from Multi-view Images Prediction Of Microvascular Invasion Of Hepatocellar Carcinoma With Contrast-Enhanced MR Using 3D CNN And LSTM Cross-modal deep metric learning with multi-task regularization Supervised Contrastive Learning Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances Multiscale conditional random fields for image labeling Kombinasi Algoritma Jaringan Syaraf Tiruan Learning Vector Quantization (LVQ) Dan Self Organizing Kohonen Pada Kecepatan Pengenalan Pola Tanda Tangan Adversarial Discriminative Domain Adaptation Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Temporal Ensembling for Semi-Supervised Learning A Triplet-Loss Embedded Deep Regressor Network for Estimating Blood Pressure Changes Using Prosodic Features A Quadruplet Loss for Enforcing Semantically Coherent Embeddings in Multi-output Classification Problems Facial Expression Recognition with Multi-scale Convolution Neural Network Unsupervised Cross-Domain Image Generation
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ArXiv International Journal of Computer Vision 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CVPR Workshops ICMR AVSS IberSPEECH NIPS 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Image Vis. Comput. COLING ISBI ICME CVPR (2) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICML ICLR ICASSP PCM (1) ICLR
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