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 7351–7400 of 10580 papers

TitleStatusHype
Predictive Learning: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction—0
Predictive Modeling of Homeless Service Assignment: A Representation Learning Approach—0
Predictive Representation Learning for Language Modeling—0
Preference or Intent? Double Disentangled Collaborative Filtering—0
A representation learning approach to probe for dynamical dark energy in matter power spectra—0
Prerequisite Relation Learning for Concepts in MOOCs—0
AdvEst: Adversarial Perturbation Estimation to Classify and Detect Adversarial Attacks against Speaker Identification—0
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification—0
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks—0
Causal Regularization—0
Learning Cross-lingual Visual Speech Representations—0
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport—0
Learning Cross-Domain Representation with Multi-Graph Neural Network—0
DisProtEdit: Exploring Disentangled Representations for Multi-Attribute Protein Editing—0
Causal Reasoning Meets Visual Representation Learning: A Prospective Study—0
A Representation Learning Approach to Feature Drift Detection in Wireless Networks—0
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery—0
Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation—0
Disentangling Singlish Discourse Particles with Task-Driven Representation—0
Learning Controllable Elements Oriented Representations for Reinforcement Learning—0
Disentangling Properties of Contrastive Methods—0
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks—0
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer—0
Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment—0
A Representation Learning Approach to Animal Biodiversity Conservation—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
Ablation Study to Clarify the Mechanism of Object Segmentation in Multi-Object Representation Learning—0
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins—0
Learning Compact Features via In-Training Representation Alignment—0
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
Learning Color Representations for Low-Light Image Enhancement—0
Learning Chemical Reaction Representation with Reactant-Product Alignment—0
Disentangling Factors of Variation Using Few Labels—0
Learning Causal Response Representations through Direct Effect Analysis—0
Disentangling Factors of Variations Using Few Labels—0
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation—0
Causal Machine Learning for Healthcare and Precision Medicine—0
Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models—0
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning—0
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity—0
Prior Learning in Introspective VAEs—0
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions—0
Learning Canonical F-Correlation Projection for Compact Multiview Representation—0
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy—0
Privacy-Aware Adversarial Network in Human Mobility Prediction—0
Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach—0
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—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