In this pa-per , we provide a general weighting framework for under-standing recent pair-based loss functions . Our contributions are three-fold : ( 1 ) we establish a General Pair Weighting ( GPW ) framework , which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis , providing a powerful tool for understanding recent pair-based loss functions ; ( 2 ) we show that with GPW , various existing pair-based methods can be compared and discussed comprehensively , with clear differences and key limitations identified ; ( 3 ) we propose a new loss called multi-similarity loss ( MS loss ) under the GPW , which is implemented in two iterative steps ( i.e. , mining and weighting ) . This allows it to fully consider three similarities for pair weighting , providing a more principled approach for collecting and weighting informative pairs . Abstract Deep metric learning methods aim to transform data features from original scattered space to a discriminative subspace in an end-to-end way , and they have shown promising results on wide applications . Triplet loss functions are the most popular models to tackle deep metric learning problem as they simultaneously enhance separability between different classes and compactness of each class in the embedded subspace . This hinders the effective utilization of state-of-the-art deep learning models in bioacoustics . To overcome this problem , the authors propose a deep metric learning-based framework that provides effective classification , even when only a small number of per-class training examples are available . The proposed framework utilizes a multiscale convolutional neural network and the proposed dynamic variant of the triplet loss to learn a transformation space where intra-class separation is minimized and inter-class separation is maximized by a dynamically increasing margin . In this paper , we propose a novel multi-view clustering method named multi-view spectral clustering network ( MvSCN ) which could be the first deep version of multi-view spectral clustering to the best of our knowledge . To deeply cluster multi-view data , MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function , where the local invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches . In addition , we enforce and reformulate an orthogonal constraint as a novel layer stacked on an embedding network for two advantages , i.e. jointly optimizing the neural network and performing matrix decomposition and avoiding trivial solutions . In this paper , we present a novel deep metric learning architecture along with variational inference incorporated in a paramet-ric multispeaker expressive text-to-speech ( TTS ) system . We proposed inverse autoregressive flow ( IAF ) as a way to perform the variational inference , thus providing flexible approximate posterior distribution . This knowledge can be naturally derived from deep metric learning model . To transfer them , we bring the learning to rank technique into deep metric learning formulation . We test our proposed DarkRank on the pedestrian re-identification task . It unifies video temporal modeling and learning to hash into one step to allow for maximum retention of information . We also introduce a deep metric learning objective called ℓ2All_loss for network training by preserving intra-class similarity and inter-class separability , and a quantization loss between the real-valued outputs and the binary codes is minimized . Extensive experiments on several challenging datasets demonstrate that SPDTH can consistently outperform state-of-the-art methods . To demonstrate the effectiveness of the proposed framework , we bootstrap a fine-grained flower dataset with 620 categories from Instagram images . The proposed deep metric learning scheme is evaluated on both our dataset and the CUB-200 - 2001 Birds dataset . Experimental evaluations show significant performance gain using dataset bootstrapping and demonstrate state-of-the-art results achieved by the proposed deep metric learning methods . In person re - ID task , deep learning methods can be classified into two classes : similarity learning and representation learning . The former is also called deep metric learning , in which image pairs or triplets are used as input to the network . In the two early works , Yi and Li use image pairs and inject part priors into the learning process . Wang use a joint learning framework to unify single - image representation and cross - image representation using a doublet or triplet CNN . Shi propose a moderate positive mining method to use deep distance metric learning for person ReID . Another novel method learns deep attributes feature for ReID with semi - supervised learning . We compare with 17 existing methods . Among them , the distance metric learning based methods are RPLM , MtMCML , Mid - level Filter , SCNCD , Similarity Learning , LADF , ITML , LMNN , KISSME , and MCML , whilst the others are discriminative subspace learning based methods including kCCA , MFA , kLFDA , and XQDA . Note that XQDA can be considered as hybrid between metric learning and subspace learning . CUHK03 : For the CUHK03 dataset , we compare our method with many existing approaches , including Filter Pair Neural Networks ( FPNN ) , Improved Deep Learning Architecture ( IDLA ) , Cross - view Quadratic Discriminant Analysis ( XQDA ) , PSD constrained asymmetric metric learning ( denoted as MLAPG ) , Sample - Specific SVM ( SS ) , Single image and Cross image representation ( SI - CI ) , Embedding Deep Metric ( EDM ) , Domain Guided Dropout ( DGD ) , DNS , S - LSTM and Gate - SCNN . On this dataset , we conduct experiments on both the detected and the labeled datasets . Many data mining algorithms developed recently are based on inductive learning methods . Very few are based on similarity-based learning . However , similarity-based learning accrues advantages , such as simple representations for concept descriptions , low incremental learning costs , small storage requirements , etc . The majority of existing work lacks an effective way to judge whether a text related to a certain event , due to the limitations of semantic representation and heuristic similarity metric . In this paper , we present an online event detection and tracking method based on similarity metric learning using neural network . Our method first trains a classification model to identify event related texts . Learning similarity of two images is an important problem in computer vision and has many potential applications . Most of the previous works focus on generating image similarities in three aspects : global feature distance computing , local feature matching , and image concepts comparison . Previous works are surveyed and they are integrated in this framework . Some applications of similarity measure learning are discussed , including fingerprint , face recognition and content-based image retrieval . A kernel-based method to learning the similarity measure is proposed and experimental result is given and discussed . A new scheme of learning similarity measure is proposed for content-based image retrieval ( CBIR ) . It learns a boundary that separates the images in the database into two clusters . The smaller the distance , the more similar are the two textures . To enable the learning of similarity for texture image retrieval , an existing SVM method is adapted to our application , but with a different similarity measure function , different texture feature vectors , and a different similarity ranking scheme for the final retrieved images based on the GLAM . We compare our approach experimentally with existing approaches by performing texture image retrieval from the Brodatz database and the Vistex database . Being able to efficiently learn appropriate similarity measures , as opposed to distances , is thus of high importance for various collections . If several works have partially addressed this problem for different applications , no previous work is known which has fully addressed it in the context of learning similarity metrics for kNN classification . This is exactly the focus of the current study . Along with the similarity learning process , the output information which is the probability distribution is used to rank similar patients . Utilizing the similarity scores , we perform personalized disease predictions , and compare the effect of different vector representations and similarity learning metrics . As basic manifold-ranking based summarization method constructs the relationships between sentences simply by the bag-of-words cosine similarity , we believe a better similarity metric will further improve the effectiveness of manifold-ranking . In this paper , we propose a joint optimization framework , which integrates the manifold-ranking process with a similarity metric learning process . The joint framework aims at learning better sentence similarity scores and better sentence ranking scores simultaneously . We compare with 17 existing methods . Among them , the distance metric learning based methods are RPLM , MtMCML , Mid - level Filter , SCNCD , Similarity Learning , LADF , ITML , LMNN , KISSME , and MCML , whilst the others are discriminative subspace learning based methods including kCCA , MFA , kLFDA , and XQDA . Note that XQDA can be considered as hybrid between metric learning and subspace learning . This pooling has different mechanism as in the 1D case , for it selects not only among compositions on different segments but also among different local matchings . This pooling strategy resembles the dynamic pooling in in a similarity learning context , but with two distinctions : 1 ) it happens on a fixed architecture and 2 ) it has much richer structure than just similarity . subsection : Some Analysis on Arc - II In this paper , we explore the use of technology to provide an electronic tutor that interacts with both teacher and student to provide a personalized and focused learning experience . Affinity Learning is an environment that captures the skills of a master teacher in a dynamic but simple technical embodiment and presents lessons and assessments online to a student . Initial results not only indicate that learning has occurred , but also distinguish male from female performance and give interesting insight into the learning process itself . Affinity diagraming is a powerful method for encouraging and capturing lateral thinking in a group environment . The Affinity+ Concept was designed to improve the collaborative brainstorm process through the use of large display surfaces in conjunction with mobile devices like smart phones and tablets . The system works by capturing the ideas digitally and allowing users to sort and group them on a large touch screen manually . An affinity research group is a comprehensive model for the creation and maintenance of dynamic , productive , and inclusive research groups in engineering and computing . The model is comprised of a set of fundamental principles and effective practices that emphasize the conscious development of studentspsila domain knowledge as well as research and professional skills . In this paper , we propose a novel affinity learning based framework for mixed data clustering , which includes : how to process data with mixed-type attributes , how to learn affinities between data points , and how to exploit the learned affinities for clustering . In the proposed framework , each original data attribute is represented with several abstract objects defined according to the specific data type and values . First , starting from the segments produced using various algorithms , the label information for basic elements of an image , segmentation-to-superpixel label , is generated . Second , the derived labels are used to construct the multiple block-diagonal priors which are integrated into the subsequent affinity learning process . Finally the segmentation result is achieved by applying the spectral clustering technique on the obtained affinity matrix . We evaluate trained models on the challenging MOT and KITTI benchmarks . Surprisingly , we find that with proper parameter learning , our simple data-association model without explicit appearance/motion reasoning is able to outperform many state-of-the-art methods that use far more complex motion features and affinity metric learning .
In this pa-per , we provide a general weighting framework for under-standing recent pair-based loss functions . Our contributions are three-fold : ( 1 ) we establish a General Pair Weighting ( GPW ) framework , which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis , providing a powerful tool for understanding recent pair-based loss functions ; ( 2 ) we show that with GPW , various existing pair-based methods can be compared and discussed comprehensively , with clear differences and key limitations identified ; ( 3 ) we propose a new loss called multi-similarity loss ( MS loss ) under the GPW , which is implemented in two iterative steps ( i.e. , mining and weighting ) . This allows it to fully consider three similarities for pair weighting , providing a more principled approach for collecting and weighting informative pairs .
Abstract Deep metric learning methods aim to transform data features from original scattered space to a discriminative subspace in an end-to-end way , and they have shown promising results on wide applications . Triplet loss functions are the most popular models to tackle deep metric learning problem as they simultaneously enhance separability between different classes and compactness of each class in the embedded subspace .
This hinders the effective utilization of state-of-the-art deep learning models in bioacoustics . To overcome this problem , the authors propose a deep metric learning-based framework that provides effective classification , even when only a small number of per-class training examples are available . The proposed framework utilizes a multiscale convolutional neural network and the proposed dynamic variant of the triplet loss to learn a transformation space where intra-class separation is minimized and inter-class separation is maximized by a dynamically increasing margin .
In this paper , we propose a novel multi-view clustering method named multi-view spectral clustering network ( MvSCN ) which could be the first deep version of multi-view spectral clustering to the best of our knowledge . To deeply cluster multi-view data , MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function , where the local invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches . In addition , we enforce and reformulate an orthogonal constraint as a novel layer stacked on an embedding network for two advantages , i.e. jointly optimizing the neural network and performing matrix decomposition and avoiding trivial solutions .
In this paper , we present a novel deep metric learning architecture along with variational inference incorporated in a paramet-ric multispeaker expressive text-to-speech ( TTS ) system . We proposed inverse autoregressive flow ( IAF ) as a way to perform the variational inference , thus providing flexible approximate posterior distribution .
This knowledge can be naturally derived from deep metric learning model . To transfer them , we bring the learning to rank technique into deep metric learning formulation . We test our proposed DarkRank on the pedestrian re-identification task .
It unifies video temporal modeling and learning to hash into one step to allow for maximum retention of information . We also introduce a deep metric learning objective called ℓ2All_loss for network training by preserving intra-class similarity and inter-class separability , and a quantization loss between the real-valued outputs and the binary codes is minimized . Extensive experiments on several challenging datasets demonstrate that SPDTH can consistently outperform state-of-the-art methods .
To demonstrate the effectiveness of the proposed framework , we bootstrap a fine-grained flower dataset with 620 categories from Instagram images . The proposed deep metric learning scheme is evaluated on both our dataset and the CUB-200 - 2001 Birds dataset . Experimental evaluations show significant performance gain using dataset bootstrapping and demonstrate state-of-the-art results achieved by the proposed deep metric learning methods .
In person re - ID task , deep learning methods can be classified into two classes : similarity learning and representation learning . The former is also called deep metric learning , in which image pairs or triplets are used as input to the network . In the two early works , Yi and Li use image pairs and inject part priors into the learning process .
Wang use a joint learning framework to unify single - image representation and cross - image representation using a doublet or triplet CNN . Shi propose a moderate positive mining method to use deep distance metric learning for person ReID . Another novel method learns deep attributes feature for ReID with semi - supervised learning .
We compare with 17 existing methods . Among them , the distance metric learning based methods are RPLM , MtMCML , Mid - level Filter , SCNCD , Similarity Learning , LADF , ITML , LMNN , KISSME , and MCML , whilst the others are discriminative subspace learning based methods including kCCA , MFA , kLFDA , and XQDA . Note that XQDA can be considered as hybrid between metric learning and subspace learning .
CUHK03 : For the CUHK03 dataset , we compare our method with many existing approaches , including Filter Pair Neural Networks ( FPNN ) , Improved Deep Learning Architecture ( IDLA ) , Cross - view Quadratic Discriminant Analysis ( XQDA ) , PSD constrained asymmetric metric learning ( denoted as MLAPG ) , Sample - Specific SVM ( SS ) , Single image and Cross image representation ( SI - CI ) , Embedding Deep Metric ( EDM ) , Domain Guided Dropout ( DGD ) , DNS , S - LSTM and Gate - SCNN . On this dataset , we conduct experiments on both the detected and the labeled datasets .
Many data mining algorithms developed recently are based on inductive learning methods . Very few are based on similarity-based learning . However , similarity-based learning accrues advantages , such as simple representations for concept descriptions , low incremental learning costs , small storage requirements , etc .
The majority of existing work lacks an effective way to judge whether a text related to a certain event , due to the limitations of semantic representation and heuristic similarity metric . In this paper , we present an online event detection and tracking method based on similarity metric learning using neural network . Our method first trains a classification model to identify event related texts .
Learning similarity of two images is an important problem in computer vision and has many potential applications . Most of the previous works focus on generating image similarities in three aspects : global feature distance computing , local feature matching , and image concepts comparison .
Previous works are surveyed and they are integrated in this framework . Some applications of similarity measure learning are discussed , including fingerprint , face recognition and content-based image retrieval . A kernel-based method to learning the similarity measure is proposed and experimental result is given and discussed .
A new scheme of learning similarity measure is proposed for content-based image retrieval ( CBIR ) . It learns a boundary that separates the images in the database into two clusters .
The smaller the distance , the more similar are the two textures . To enable the learning of similarity for texture image retrieval , an existing SVM method is adapted to our application , but with a different similarity measure function , different texture feature vectors , and a different similarity ranking scheme for the final retrieved images based on the GLAM . We compare our approach experimentally with existing approaches by performing texture image retrieval from the Brodatz database and the Vistex database .
Being able to efficiently learn appropriate similarity measures , as opposed to distances , is thus of high importance for various collections . If several works have partially addressed this problem for different applications , no previous work is known which has fully addressed it in the context of learning similarity metrics for kNN classification . This is exactly the focus of the current study .
Along with the similarity learning process , the output information which is the probability distribution is used to rank similar patients . Utilizing the similarity scores , we perform personalized disease predictions , and compare the effect of different vector representations and similarity learning metrics .
As basic manifold-ranking based summarization method constructs the relationships between sentences simply by the bag-of-words cosine similarity , we believe a better similarity metric will further improve the effectiveness of manifold-ranking . In this paper , we propose a joint optimization framework , which integrates the manifold-ranking process with a similarity metric learning process . The joint framework aims at learning better sentence similarity scores and better sentence ranking scores simultaneously .
We compare with 17 existing methods . Among them , the distance metric learning based methods are RPLM , MtMCML , Mid - level Filter , SCNCD , Similarity Learning , LADF , ITML , LMNN , KISSME , and MCML , whilst the others are discriminative subspace learning based methods including kCCA , MFA , kLFDA , and XQDA . Note that XQDA can be considered as hybrid between metric learning and subspace learning .
This pooling has different mechanism as in the 1D case , for it selects not only among compositions on different segments but also among different local matchings . This pooling strategy resembles the dynamic pooling in in a similarity learning context , but with two distinctions : 1 ) it happens on a fixed architecture and 2 ) it has much richer structure than just similarity . subsection : Some Analysis on Arc - II
In this paper , we explore the use of technology to provide an electronic tutor that interacts with both teacher and student to provide a personalized and focused learning experience . Affinity Learning is an environment that captures the skills of a master teacher in a dynamic but simple technical embodiment and presents lessons and assessments online to a student . Initial results not only indicate that learning has occurred , but also distinguish male from female performance and give interesting insight into the learning process itself .
Affinity diagraming is a powerful method for encouraging and capturing lateral thinking in a group environment . The Affinity+ Concept was designed to improve the collaborative brainstorm process through the use of large display surfaces in conjunction with mobile devices like smart phones and tablets . The system works by capturing the ideas digitally and allowing users to sort and group them on a large touch screen manually .
An affinity research group is a comprehensive model for the creation and maintenance of dynamic , productive , and inclusive research groups in engineering and computing . The model is comprised of a set of fundamental principles and effective practices that emphasize the conscious development of studentspsila domain knowledge as well as research and professional skills .
In this paper , we propose a novel affinity learning based framework for mixed data clustering , which includes : how to process data with mixed-type attributes , how to learn affinities between data points , and how to exploit the learned affinities for clustering . In the proposed framework , each original data attribute is represented with several abstract objects defined according to the specific data type and values .
First , starting from the segments produced using various algorithms , the label information for basic elements of an image , segmentation-to-superpixel label , is generated . Second , the derived labels are used to construct the multiple block-diagonal priors which are integrated into the subsequent affinity learning process . Finally the segmentation result is achieved by applying the spectral clustering technique on the obtained affinity matrix .
We evaluate trained models on the challenging MOT and KITTI benchmarks . Surprisingly , we find that with proper parameter learning , our simple data-association model without explicit appearance/motion reasoning is able to outperform many state-of-the-art methods that use far more complex motion features and affinity metric learning .
Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning Deep metric learning via subtype fuzzy clustering Multiscale CNN based Deep Metric Learning for Bioacoustic Classification: Overcoming Training Data Scarcity Using Dynamic Triplet Loss Multi-view Spectral Clustering Network Transfer learning of the expressivity using flow metric learning in multispeaker text-to-speech synthesis DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer Video Retrieval with Similarity-Preserving Deep Temporal Hashing Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop SVDNet for Pedestrian Retrieval Pose-Driven Deep Convolutional Model for Person Re-identification Learning a Discriminative Null Space for Person Re-identification Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Rough Sets Similarity-Based Learning from Databases Online event detection and tracking in social media based on neural similarity metric learning Class Agnostic Image Common Object Detection Kernel-based similarity learning Learning similarity measure for natural image retrieval with relevance feedback Similarity measure and learning with gray level aura matrices (GLAM) for texture image retrieval Generalized Cosine and Similarity Metrics: A Supervised Learning Approach based on Nearest Neighbors. (Mesures de similarité et cosinus généralisé : une approche d'apprentissage supervisé fondée sur les k plus proches voisins) Personalized disease prediction using a CNN-based similarity learning method Joint Matrix Factorization and Manifold-Ranking for Topic-Focused Multi-Document Summarization Learning a Discriminative Null Space for Person Re-identification Convolutional Neural Network Architectures for Matching Natural Language Sentences Affinity Learning in Mathematics Affinity+: Semi-Structured Brainstorming on Large Displays Workshop - engaging undergraduate students in research using the Affinity Research Group model Affinity Learning for Mixed Data Clustering Object segmentation using low-rank representation with multiple block-diagonal priors Learning Optimal Parameters For Multi-target Tracking
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IJCAI 2017 IEEE International Conference on Computer Vision (ICCV) 2017 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) KDD ISI CVPR (1) BIBM SIGIR 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS IJCAI ICPR BMVC
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