Update and add index

terminal/metriclearning

@@ -1,9 +1,9 @@
-                                                                                    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.
 
@@ -21,9 +21,8 @@
 
 
 
-▐ 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.
 
 
 
@@ -55,8 +54,7 @@
 
 
 
-▐ CLIP offers state-of-the-art zero-shot image classification and image retrieval with a natural language query. See demo 
-▐ (https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb).
+▐ CLIP offers state-of-the-art zero-shot image classification and image retrieval with a natural language query. See demo (https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb).
 
 
 
@@ -75,30 +73,28 @@
 
 
 ▐ 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.
 
@@ -106,9 +102,8 @@
 
 
 
-▐ 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.
 
 
 
@@ -119,16 +114,15 @@
 
 
 
-▐ 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.
 
 
 
@@ -140,8 +134,7 @@
 
 
 
-▐ 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).
 
 
 
@@ -153,8 +146,8 @@
 
 
 
-▐ 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 ✍️
@@ -181,17 +174,15 @@
 
 
 
-▐ 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.
 
 
 
@@ -202,8 +193,7 @@
 
 
 
-▐ 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.
 
 
 
@@ -240,15 +230,14 @@
  - 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.
 
 
 
@@ -269,8 +258,8 @@
 
 
 
-▐ 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).
 
 
 
@@ -291,15 +280,14 @@
 
 
 
-▐ 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))
 
 
 
@@ -314,16 +302,15 @@
 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 
-▐ (https://medium.com/@maksym.bekuzarov/losses-explained-contrastive-loss-f8f57fe32246) to better understand its importance for metric learning.
+▐ 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.
 
 
 
@@ -342,8 +329,8 @@
  
 ▐ 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.
 
 
 
@@ -355,15 +342,15 @@
 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.
 
 
 
@@ -383,8 +370,8 @@
 
 
 
-▐ 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.
 
 
 
@@ -398,16 +385,15 @@
 
 
 
-▐ 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,