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 7001–7050 of 10580 papers

TitleStatusHype
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning—0
On the Provable Advantage of Unsupervised Pretraining—0
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Feature Disentanglement of Robot Trajectories—0
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning—0
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting—0
Bird Vocalization Embedding Extraction Using Self-Supervised Disentangled Representation Learning—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
On the Theory of Transfer Learning: The Importance of Task Diversity—0
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes—0
Contrastive Attention Maps for Self-Supervised Co-Localization—0
H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing—0
On the Unintended Social Bias of Training Language Generation Models with News Articles—0
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture—0
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Ontologically Grounded Multi-sense Representation Learning for Semantic Vector Space Models—0
On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification—0
On unsupervised-supervised risk and one-class neural networks—0
HHTrack: Hyperspectral Object Tracking Using Hybrid Attention—0
OOD Aware Supervised Contrastive Learning—0
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors—0
DeepHEN: quantitative prediction essential lncRNA genes and rethinking essentialities of lncRNA genes—0
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning—0
Feature Projection for Improved Text Classification—0
OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning—0
Open Problem: Active Representation Learning—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
BioSequence2Vec: Efficient Embedding Generation For Biological Sequences—0
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology—0
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation—0
How to represent a word and predict it, too: Improving tied architectures for language modelling—0
Open-World Dynamic Prompt and Continual Visual Representation Learning—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
A Neural Autoregressive Topic Model—0
A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis—0
Piece-wise Matching Layer in Representation Learning for ECG Classification—0
How Robust is Unsupervised Representation Learning to Distribution Shift?—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
OpticE: A Coherence Theory-Based Model for Link Prediction—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization—0
Contrastive Continual Learning with Feature Propagation—0
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations—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