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terminal/metriclearning

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-                                                                                              awesome-metric-learning
+                                                                                    awesome-metric-learning
 😎 Awesome list about practical Metric Learning and its applications
 
 Motivation 🤓
-At Qdrant, we have one goal: make metric learning more practical. This listing is in line with this purpose, and we aim at providing a concise yet useful list of awesomeness around metric learning. It is 
-intended to be inspirational for productivity rather than serve as a full bibliography.
+At Qdrant, we have one goal: make metric learning more practical. This listing is in line with this purpose, and we aim at providing a concise yet useful list of awesomeness around metric 
+learning. It is intended to be inspirational for productivity rather than serve as a full bibliography.
 
 If you find it useful or like it in some other way, you may want to join our Discord server, where we are running a paper reading club on metric learning.
 
@@ -21,8 +21,9 @@
 
 
 
-▐ It has proceeding guides for supervised (http://contrib.scikit-learn.org/metric-learn/supervised.html), weakly supervised (http://contrib.scikit-learn.org/metric-learn/weakly_supervised.html) and unsupervised 
-▐ (http://contrib.scikit-learn.org/metric-learn/unsupervised.html) metric learning algorithms in metric_learn (http://contrib.scikit-learn.org/metric-learn/metric_learn.html) package.
+▐ It has proceeding guides for supervised (http://contrib.scikit-learn.org/metric-learn/supervised.html), weakly supervised 
+▐ (http://contrib.scikit-learn.org/metric-learn/weakly_supervised.html) and unsupervised (http://contrib.scikit-learn.org/metric-learn/unsupervised.html) metric learning algorithms in 
+▐ metric_learn (http://contrib.scikit-learn.org/metric-learn/metric_learn.html) package.
 
 
 
@@ -74,28 +75,30 @@
 
 
 ▐ TensorFlow Hub offers a collection of pretrained models from the paper Large Dual Encoders Are Generalizable Retrievers (https://arxiv.org/abs/2112.07899).
-▐ GTR models are first initialized from a pre-trained T5 checkpoint. They are then further pre-trained with a set of community question-answer pairs. Finally, they are fine-tuned on the MS Marco dataset.
+▐ GTR models are first initialized from a pre-trained T5 checkpoint. They are then further pre-trained with a set of community question-answer pairs. Finally, they are fine-tuned on the MS 
+▐ Marco dataset.
 ▐ The two encoders are shared so the GTR model functions as a single text encoder. The input is variable-length English text and the output is a 768-dimensional vector.
 
 
 
 
 
-▐ The method and pretrained models found in Flair go beyond zero-shot sequence classification and offers zero-shot span tagging abilities for tasks such as named entity recognition and part of speech tagging.
+▐ The method and pretrained models found in Flair go beyond zero-shot sequence classification and offers zero-shot span tagging abilities for tasks such as named entity recognition and part 
+▐ of speech tagging.
 
 
 
 
 
-▐ It leverages HuggingFace Transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics while keeping important words in the topic descriptions. It supports guided, (semi-) 
-▐ supervised, and dynamic topic modeling beautiful visualizations.
+▐ It leverages HuggingFace Transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics while keeping important words in the topic descriptions. It supports 
+▐ guided, (semi-) supervised, and dynamic topic modeling beautiful visualizations.
 
 
 
 
 
-▐ Identification of substances based on spectral analysis plays a vital role in forensic science. Similarly, the material identification process is of paramount importance for malfunction reasoning in 
-▐ manufacturing sectors and materials research.
+▐ Identification of substances based on spectral analysis plays a vital role in forensic science. Similarly, the material identification process is of paramount importance for malfunction 
+▐ reasoning in manufacturing sectors and materials research.
 ▐ This models enables to identify materials with deep metric learning applied to X-Ray Diffraction (XRD) spectrum. Read this post 
 ▐ (https://towardsdatascience.com/automatic-spectral-identification-using-deep-metric-learning-with-1d-regnet-and-adacos-8b7fb36f2d5f) for more background.
 
@@ -103,9 +106,9 @@
 
 
 
-▐ Different from typical information retrieval tasks, code search requires to bridge the semantic gap between the programming language and natural language, for better describing intrinsic concepts and 
-▐ semantics. The repository provides the pretrained models and source code for Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus (https://arxiv.org/abs/2201.11313), where they apply several
-▐ tricks to achieve this.
+▐ Different from typical information retrieval tasks, code search requires to bridge the semantic gap between the programming language and natural language, for better describing intrinsic 
+▐ concepts and semantics. The repository provides the pretrained models and source code for Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus 
+▐ (https://arxiv.org/abs/2201.11313), where they apply several tricks to achieve this.
 
 
 
@@ -116,16 +119,16 @@
 
 
 
-▐ State-of-the-art methods are incapable of leveraging attributes from different types of items and thus suffer from data sparsity problems because it is quite challenging to represent items with different 
-▐ feature spaces jointly. To tackle this problem, they propose a kernel-based neural network, namely deep unified representation (DURation) for heterogeneous recommendation, to jointly model unified 
-▐ representations of heterogeneous items while preserving their original feature space topology structures. See paper (https://arxiv.org/abs/2201.05861).
+▐ State-of-the-art methods are incapable of leveraging attributes from different types of items and thus suffer from data sparsity problems because it is quite challenging to represent items 
+▐ with different feature spaces jointly. To tackle this problem, they propose a kernel-based neural network, namely deep unified representation (DURation) for heterogeneous recommendation, to
+▐ jointly model unified representations of heterogeneous items while preserving their original feature space topology structures. See paper (https://arxiv.org/abs/2201.05861).
 
 
 
 
 
-▐ It provides the implementation of Item2Vec: Neural Item Embedding for Collaborative Filtering (https://arxiv.org/abs/1603.04259), wrapped as a sklearn estimator compatible with GridSearchCV and BayesSearchCV 
-▐ for hyperparameter tuning.
+▐ It provides the implementation of Item2Vec: Neural Item Embedding for Collaborative Filtering (https://arxiv.org/abs/1603.04259), wrapped as a sklearn estimator compatible with GridSearchCV
+▐ and BayesSearchCV for hyperparameter tuning.
 
 
 
@@ -137,7 +140,8 @@
 
 
 
-▐ It searches phrase-level answers to your questions in real-time or retrieves passages for downstream tasks. Check out demo (http://densephrases.korea.ac.kr/), or see paper (https://arxiv.org/abs/2109.08133).
+▐ It searches phrase-level answers to your questions in real-time or retrieves passages for downstream tasks. Check out demo (http://densephrases.korea.ac.kr/), or see paper 
+▐ (https://arxiv.org/abs/2109.08133).
 
 
 
@@ -149,8 +153,8 @@
 
 
 
-▐ Application of the SimCLR method to musical data with out-of-domain generalization in million-scale music classification. See demo (https://spijkervet.github.io/CLMR/examples/clmr-onnxruntime-web/) or paper 
-▐ (https://arxiv.org/abs/2103.09410).
+▐ Application of the SimCLR method to musical data with out-of-domain generalization in million-scale music classification. See demo 
+▐ (https://spijkervet.github.io/CLMR/examples/clmr-onnxruntime-web/) or paper (https://arxiv.org/abs/2103.09410).
 
 
 Case Studies ✍️
@@ -177,15 +181,17 @@
 
 
 
-▐ Quaterion is a framework for fine-tuning similarity learning models. The framework closes the "last mile" problem in training models for semantic search, recommendations, anomaly detection, extreme 
-▐ classification, matching engines, e.t.c. It is designed to combine the performance of pre-trained models with specialization for the custom task while avoiding slow and costly training.
+▐ Quaterion is a framework for fine-tuning similarity learning models. The framework closes the "last mile" problem in training models for semantic search, recommendations, anomaly detection,
+▐ extreme classification, matching engines, e.t.c. It is designed to combine the performance of pre-trained models with specialization for the custom task while avoiding slow and costly 
+▐ training.
 
 
 
  - A library for 
 sentence-level embeddings. 
 
-▐ Developed on top of the well-known Transformers (https://github.com/huggingface/transformers) library, it provides an easy way to finetune Transformer-based models to obtain sequence-level embeddings.
+▐ Developed on top of the well-known Transformers (https://github.com/huggingface/transformers) library, it provides an easy way to finetune Transformer-based models to obtain sequence-level 
+▐ embeddings.
 
 
 
@@ -196,7 +202,8 @@
 
 
 
-▐ The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase identification.
+▐ The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase 
+▐ identification.
 
 
 
@@ -233,15 +240,15 @@
  - A Python implementation of a number of popular 
 recommender algorithms. 
 
-▐ It supports incorporating user and item features to the traditional matrix factorization. It represents users and items as a sum of the latent representations of their features, thus achieving a better 
-▐ generalization.
+▐ It supports incorporating user and item features to the traditional matrix factorization. It represents users and items as a sum of the latent representations of their features, thus 
+▐ achieving a better generalization.
 
 
 
 
 
-▐ It provides efficient multicore and memory-independent implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), 
-▐ Hierarchical Dirichlet Process (HDP) or word2vec.
+▐ It provides efficient multicore and memory-independent implementations of popular algorithms, such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), 
+▐ Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec.
 
 
 
@@ -262,8 +269,8 @@
 
 
 
-▐ It allows you to visualize the embedding space selecting explicitly the axis through algebraic formulas on the embeddings (like king-man+woman) and highlight specific items in the embedding space. It also 
-▐ supports implicit axes via PCA and t-SNE. See paper (https://arxiv.org/abs/1905.12099).
+▐ It allows you to visualize the embedding space selecting explicitly the axis through algebraic formulas on the embeddings (like king-man+woman) and highlight specific items in the embedding
+▐ space. It also supports implicit axes via PCA and t-SNE. See paper (https://arxiv.org/abs/1905.12099).
 
 
 
@@ -284,14 +291,15 @@
 
 
 
-▐ It is not the fastest ANN algorithm but achieves memory efficiency thanks to various quantization and indexing methods such as IVF, PQ, and IVF-PQ. (Tutorial (https://www.pinecone.io/learn/faiss-tutorial/))
+▐ It is not the fastest ANN algorithm but achieves memory efficiency thanks to various quantization and indexing methods such as IVF, PQ, and IVF-PQ. (Tutorial 
+▐ (https://www.pinecone.io/learn/faiss-tutorial/))
 
 
 
 
 
-▐ It is still one of the fastest ANN algorithms out there, requiring relatively a higher memory usage. (Paper: Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World 
-▐ graphs (https://arxiv.org/abs/1603.09320))
+▐ It is still one of the fastest ANN algorithms out there, requiring relatively a higher memory usage. (Paper: Efficient and robust approximate nearest neighbor search using Hierarchical 
+▐ Navigable Small World graphs (https://arxiv.org/abs/1603.09320))
 
 
 
@@ -306,16 +314,16 @@
 Dimensionality Reduction by 
 Learning an Invariant Mapping
 
-▐ Published by Yann Le Cun et al. (2005), its main focus was on dimensionality reduction. However, the method proposed has excellent properties for metric learning such as preserving neighbourhood relationships 
-▐ and generalization to unseen data, and it has extensive applications with a great number of variations ever since. It is advised that you read this great post 
+▐ Published by Yann Le Cun et al. (2005), its main focus was on dimensionality reduction. However, the method proposed has excellent properties for metric learning such as preserving 
+▐ neighbourhood relationships and generalization to unseen data, and it has extensive applications with a great number of variations ever since. It is advised that you read this great post 
 ▐ (https://medium.com/@maksym.bekuzarov/losses-explained-contrastive-loss-f8f57fe32246) to better understand its importance for metric learning.
 
 
 
 
 
-▐ The paper introduces Triplet Loss, which can be seen as the "ImageNet moment" for deep metric learning. It is still one of the state-of-the-art methods and has a great number of applications in almost any data
-▐ modality.
+▐ The paper introduces Triplet Loss, which can be seen as the "ImageNet moment" for deep metric learning. It is still one of the state-of-the-art methods and has a great number of 
+▐ applications in almost any data modality.
 
 
 
@@ -334,8 +342,8 @@
  
 ▐ Supervised metric learning without pairs or triplets.
 
-▐ Although it is originally designed for the face recognition task, this loss function achieves state-of-the-art results in many other metric learning problems with a simpler and faster data feeding. It is also 
-▐ robust against unclean and unbalanced data when modified with sub-centers and a dynamic margin.
+▐ Although it is originally designed for the face recognition task, this loss function achieves state-of-the-art results in many other metric learning problems with a simpler and faster data 
+▐ feeding. It is also robust against unclean and unbalanced data when modified with sub-centers and a dynamic margin.
 
 
 
@@ -347,15 +355,15 @@
 VICReg: Variance-Invariance-Covariance Regularization for 
 Self-Supervised Learning
 
-▐ The paper introduces a method that explicitly avoids the collapse problem in high dimensions with a simple regularization term on the variance of the embeddings along each dimension individually. This new term
-▐ can be incorporated into other methods to stabilize the training and performance improvements.
+▐ The paper introduces a method that explicitly avoids the collapse problem in high dimensions with a simple regularization term on the variance of the embeddings along each dimension 
+▐ individually. This new term can be incorporated into other methods to stabilize the training and performance improvements.
 
 
 
 
 
-▐ The paper proposes using the mean centroid representation during training and retrieval for robustness against outliers and more stable features. It further reduces retrieval time and storage requirements, 
-▐ making it suitable for production deployments.
+▐ The paper proposes using the mean centroid representation during training and retrieval for robustness against outliers and more stable features. It further reduces retrieval time and 
+▐ storage requirements, making it suitable for production deployments.
 
 
 
@@ -375,8 +383,8 @@
 
 
 
-▐ They also incorporates annotated pairs from natural language inference datasets into their contrastive learning framework in a supervised setting, showing that contrastive learning objective regularizes 
-▐ pre-trained embeddings’ anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available.
+▐ They also incorporates annotated pairs from natural language inference datasets into their contrastive learning framework in a supervised setting, showing that contrastive learning 
+▐ objective regularizes pre-trained embeddings’ anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available.
 
 
 
@@ -390,15 +398,16 @@
 
 
 
-▐ Mining informative negative instances are of central importance to deep metric learning (DML), however this task is intrinsically limited by mini-batch training, where only a mini-batch of instances is 
-▐ accessible at each iteration. In this paper, we identify a "slow drift" phenomena by observing that the embedding features drift exceptionally slow even as the model parameters are updating throughout the 
-▐ training process. This suggests that the features of instances computed at preceding iterations can be used to considerably approximate their features extracted by the current model.
+▐ Mining informative negative instances are of central importance to deep metric learning (DML), however this task is intrinsically limited by mini-batch training, where only a mini-batch of 
+▐ instances is accessible at each iteration. In this paper, we identify a "slow drift" phenomena by observing that the embedding features drift exceptionally slow even as the model parameters
+▐ are updating throughout the training process. This suggests that the features of instances computed at preceding iterations can be used to considerably approximate their features extracted 
+▐ by the current model.
 
 
 
 Datasets ℹ️
-▐ Practitioners can use any labeled or unlabelled data for metric learning with an appropriate method chosen. However, some datasets are particularly important in the literature for benchmarking or other ways, 
-▐ and we list them in this section.
+▐ Practitioners can use any labeled or unlabelled data for metric learning with an appropriate method chosen. However, some datasets are particularly important in the literature for 
+▐ benchmarking or other ways, and we list them in this section.
 
 
  - The Stanford Natural Language Inference Corpus,