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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6901–6950 of 10580 papers

TitleStatusHype
Fairness-Aware Node Representation Learning—0
Deep-Learning-Assisted Analysis of Cataract Surgery Videos—0
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages—0
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
On Invariance and Selectivity in Representation Learning—0
Deep Learning Approach on Information Diffusion in Heterogeneous Networks—0
Continual Learning of Nonlinear Independent Representations—0
On learning and representing social meaning in NLP: a sociolinguistic perspective—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs—0
Interpretable Deep Representation Learning from Temporal Multi-view Data—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Bitcoin Transaction Forecasting with Deep Network Representation Learning—0
On Linear Identifiability of Learned Representations—0
10 Years of Fair Representations: Challenges and Opportunities—0
PASS: Patch-Aware Self-Supervision for Vision Transformer—0
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics—0
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners—0
Fair Representation Learning through Implicit Path Alignment—0
Hybrid deep learning methods for phenotype prediction from clinical notes—0
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks—0
Deep Inverse Feature Learning: A Representation Learning of Error—0
Partially latent factors based multi-view subspace learning—0
Online Hybrid Lightweight Representations Learning: Its Application to Visual Tracking—0
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning—0
Online Limited Memory Neural-Linear Bandits—0
Hybrid Active Inference—0
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling—0
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs—0
Online Representation Learning in Recurrent Neural Language Models—0
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder—0
Online Representation Learning with Single and Multi-layer Hebbian Networks for Image Classification—0
Particle Trajectory Representation Learning with Masked Point Modeling—0
Human Semantic Parsing for Person Re-identification—0
Human-oriented Representation Learning for Robotic Manipulation—0
On minimal variations for unsupervised representation learning—0
On Modeling Sense Relatedness in Multi-prototype Word Embedding—0
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level—0
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks—0
On Mutual Information in Contrastive Learning for Visual Representations—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
On Negative Sampling for Audio-Visual Contrastive Learning from Movies—0
An Identity-Preserved Framework for Human Motion Transfer—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
On the Arrow of Inference—0
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"—0
On provable privacy vulnerabilities of graph representations—0
Human Gaze Boosts Object-Centered Representation Learning—0
On Representation Learning for Scientific News Articles Using Heterogeneous Knowledge Graphs—0
HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning—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