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 5301–5350 of 10580 papers

TitleStatusHype
Aligning Robot and Human Representations—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification—0
FARM: Functional Group-Aware Representations for Small Molecules—0
Continual Unsupervised Representation Learning—0
FAM: Visual Explanations for the Feature Representations From Deep Convolutional Networks—0
Continual State Representation Learning for Reinforcement Learning using Generative Replay—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
Fair Sufficient Representation Learning—0
Fair Representation Learning using Interpolation Enabled Disentanglement—0
Fair Representation Learning through Implicit Path Alignment—0
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer—0
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics—0
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records—0
Continual Lifelong Causal Effect Inference with Real World Evidence—0
Audio-visual Representation Learning for Anomaly Events Detection in Crowds—0
Aligning Multimodal Representations through an Information Bottleneck—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
Continual Learning of Nonlinear Independent Representations—0
Fairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show—0
Fairness by Learning Orthogonal Disentangled Representations—0
Continual Learning for Motion Prediction Model via Meta-Representation Learning and Optimal Memory Buffer Retention Strategy—0
Fairness-Aware Node Representation Learning—0
FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints—0
Audio-to-Image Cross-Modal Generation—0
Aligning Audio-Visual Joint Representations with an Agentic Workflow—0
Fair Interpretable Representation Learning with Correction Vectors—0
Fair Interpretable Learning via Correction Vectors—0
Fair Inference for Discrete Latent Variable Models—0
Fair Group-Shared Representations with Normalizing Flows—0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
FairGen: Towards Fair Graph Generation—0
Continual Causal Inference with Incremental Observational Data—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
Audio Representation Learning by Distilling Video as Privileged Information—0
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction—0
Contextures: The Mechanism of Representation Learning—0
Factors of Transferability for a Generic ConvNet Representation—0
Factorized Visual Tokenization and Generation—0
Contextures: Representations from Contexts—0
Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models—0
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition—0
Factorial Hidden Markov Models for Learning Representations of Natural Language—0
Factor Graph Neural Networks—0
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive 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