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 8251–8300 of 10580 papers

TitleStatusHype
Coarsely-Labeled Data for Better Few-Shot TransferCode0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation—0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
Clearing the Path for Truly Semantic Representation Learning—0
Dual Graph Complementary Network—0
Leveraging affinity cycle consistency to isolate factors of variation in learned representations—0
Learning Visual Representation from Human Interactions—0
A Flexible Framework for Discovering Novel Categories with Contrastive Learning—0
Learning Task-Relevant Features via Contrastive Input Morphing—0
Learning Subgoal Representations with Slow Dynamics—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
When Is Generalizable Reinforcement Learning Tractable?—0
Learning Private Representations with Focal Entropy—0
Sequence Metric Learning as Synchronization of Recurrent Neural Networks—0
Learning Latent Topology for Graph Matching—0
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.—0
Learning Flexible Visual Representations via Interactive Gameplay—0
Switchable K-Class Hyperplanes for Noise-Robust Representation Learning—0
Learning disentangled representations with the Wasserstein Autoencoder—0
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions—0
UserBERT: Self-supervised User Representation Learning—0
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning—0
Disentangled Representation for Age-Invariant Face Recognition: A Mutual Information Minimization Perspective—0
LapsCore: Language-Guided Person Search via Color Reasoning—0
Knowledge distillation via softmax regression representation learning—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Invariant Representations for Reinforcement Learning without Reconstruction—0
Invariant Causal Representation Learning—0
Self-supervised representation learning via adaptive hard-positive mining—0
Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble—0
Improving Generalizability of Protein Sequence Models via Data Augmentations—0
Deep Q-Learning with Low Switching Cost—0
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw—0
How Benign is Benign Overfitting ?—0
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
Hierarchical Disentangled Representation Learning for Outdoor Illumination Estimation and Editing—0
Decomposing Mutual Information for Representation Learning—0
Guiding Representation Learning in Deep Generative Models with Policy Gradients—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
Toward Understanding Supervised Representation Learning with RKHS and GAN—0
Transferable Unsupervised Robust Representation Learning—0
Self-Born Wiring for Neural Trees—0
Language-Mediated, Object-Centric Representation Learning—0
Deep Graph Generators: A Survey—0
Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention—0
AttrE2vec: Unsupervised Attributed Edge Representation Learning—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