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 7001–7050 of 10580 papers

TitleStatusHype
Center-wise Local Image Mixture For Contrastive Representation Learning—0
On the Provable Advantage of Unsupervised Pretraining—0
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images—0
Recommendation with Attribute-aware Product Networks: A Representation Learning Model—0
Learning List-Level Domain-Invariant Representations for Ranking—0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar—0
A Role of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents—0
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups—0
Learning Less-Overlapping Representations—0
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation—0
Learning Latent Topology for Graph Matching—0
On the Unintended Social Bias of Training Language Generation Models with News Articles—0
Learning latent state representation for speeding up exploration—0
Learning latent representations for operational nitrogen response rate prediction—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
DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation—0
On unsupervised-supervised risk and one-class neural networks—0
Census-Independent Population Estimation using Representation Learning—0
OOD Aware Supervised Contrastive Learning—0
Learning Language Representations with Logical Inductive Bias—0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Learning Job Titles Similarity from Noisy Skill Labels—0
Learning Invariant Representation via Contrastive Feature Alignment for Clutter Robust SAR Target Recognition—0
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning—0
Cell Variational Information Bottleneck Network—0
OpenMixup: Open Mixup Toolbox and Benchmark for Visual Representation Learning—0
Open Problem: Active Representation Learning—0
Learning Invariant Representation and Risk Minimized for Unsupervised Accent Domain Adaptation—0
Learning Interpretable Style Embeddings via Prompting LLMs—0
CellSegmenter: unsupervised representation learning and instance segmentation of modular images—0
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology—0
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields—0
AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses—0
About contrastive unsupervised representation learning for classification and its convergence—0
Learning Interpretable Fair Representations—0
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models—0
Learning Internal Representations (COLT 1995)—0
Learning Internal Representations (PhD Thesis)—0
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets—0
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data—0
Learning in Factored Domains with Information-Constrained Visual Representations—0
Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks—0
OpticE: A Coherence Theory-Based Model for Link Prediction—0
DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction—0
CEIR: Concept-based Explainable Image Representation Learning—0
Learning Image Representations by Completing Damaged Jigsaw Puzzles—0
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic 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