SOTAVerified

Representation Learning

Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.

Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.

Representation learning can be divided into:

  • Supervised representation learning: learning representations on task A using annotated data and used to solve task B
  • Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.

More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.

Here are some additional readings to go deeper on the task:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 6151–6200 of 10580 papers

TitleStatusHype
FARE: Provably Fair Representation Learning with Practical CertificatesCode0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
Disentanglement of Correlated Factors via Hausdorff Factorized Support—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
The Hidden Uniform Cluster Prior in Self-Supervised Learning—0
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning—0
A Lower Bound of Hash Codes' PerformanceCode0
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property—0
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness—0
Entity Aware Negative Sampling with Auxiliary Loss of False Negative Prediction for Knowledge Graph EmbeddingCode0
Language Agnostic Multilingual Information Retrieval with Contrastive LearningCode0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding—0
Pre-Training Representations of Binary Code Using Contrastive Learning—0
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble LearningCode0
Robust and Controllable Object-Centric Learning through Energy-based Models—0
Improving Dense Contrastive Learning with Dense Negative Pairs—0
Robust Diversified Graph Contrastive Network for Incomplete Multi-view ClusteringCode0
Contrastive Video-Language Learning with Fine-grained Frame Sampling—0
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing—0
Meta-Principled Family of Hyperparameter Scaling Strategies—0
TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced RecommendationCode0
A Simple Baseline that Questions the Use of Pretrained-Models in Continual LearningCode0
DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos—0
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER—0
On the Forward Invariance of Neural ODEs—0
Turbo Training with Token Dropout—0
Contrastive Representation Learning for Conversational Question Answering over Knowledge GraphsCode0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Better Pre-Training by Reducing Representation Confusion—0
Robustness of Unsupervised Representation Learning without LabelsCode0
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
Uplifting Message Passing Neural Network with Graph Original Information—0
Towards Real-Time Temporal Graph LearningCode0
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information—0
Dynamic Latent Separation for Deep Learning—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—0
Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision—0
Domain-Specific Word Embeddings with Structure PredictionCode0
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Differentiable Mathematical Programming for Object-Centric Representation Learning—0
Antibody Representation Learning for Drug Discovery—0
Vision+X: A Survey on Multimodal Learning in the Light of Data—0
Representing Spatial Trajectories as Distributions—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative SamplesCode0
Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength—0
Green Learning: Introduction, Examples and Outlook—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified