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 5251–5300 of 10580 papers

TitleStatusHype
Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation—0
LCOT: Linear circular optimal transport—0
E^3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
BrewCLIP: A Bifurcated Representation Learning Framework for Audio-Visual Retrieval—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
Learned feature representations are biased by complexity, learning order, position, and more—0
Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Query Obfuscation Semantic Decomposition—0
Demo2Vec: Learning Region Embedding with Demographic Information—0
Learning Universal Representations from Word to Sentence—0
Learning Actionable Representations with Goal Conditioned Policies—0
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using β-VAE—0
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback—0
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Semantic Implicit Neural Scene Representations With Semi-Supervised Training—0
Learning and Retrieval from Prior Data for Skill-based Imitation Learning—0
Learning an Ensemble of Deep Fingerprint Representations—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring—0
Inferential SIR-GN: Scalable Graph Representation Learning—0
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning—0
DeMIAN: Deep Modality Invariant Adversarial Network—0
Uplifting Message Passing Neural Network with Graph Original Information—0
Learning Attribute and Class-Specific Representation Duet for Fine-Grained Fashion Analysis—0
Learning unbiased features—0
Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
Inductive Topic Variational Graph Auto-Encoder for Text Classification—0
Break The Spell Of Total Correlation In betaTCVAE—0
Learning Behavior Representations Through Multi-Timescale Bootstrapping—0
A Non-negative VAE:the Generalized Gamma Belief Network—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Learning Better Visual Representations for Weakly-Supervised Object Detection Using Natural Language Supervision—0
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization—0
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation—0
Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting—0
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis—0
Learning by Reconstruction Produces Uninformative Features For Perception—0
Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation—0
Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach—0
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks—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