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 8951–9000 of 10580 papers

TitleStatusHype
Deep Clustering and Representation Learning that Preserves Geometric Structures—0
10 Years of Fair Representations: Challenges and Opportunities—0
Deep Clustering by Semantic Contrastive Learning—0
Deep clustering with fusion autoencoder—0
Deep Clustering with Measure Propagation—0
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
Deep Concept Identification for Generative Design—0
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound—0
Deep Contextual Recurrent Residual Networks for Scene Labeling—0
Understanding Deep Contrastive Learning via Coordinate-wise Optimization—0
Deep Convolutional Transform Learning -- Extended version—0
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
Deep Descriptive Clustering—0
Deep Determinantal Point Process for Large-Scale Multi-Label Classification—0
Deep Dictionary Learning with An Intra-class Constraint—0
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation—0
Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers—0
Deep Discriminative Representation Learning with Attention Map for Scene Classification—0
Deep Dive into Semi-Supervised ELBO for Improving Classification Performance—0
Deep Domain Generalization via Conditional Invariant Adversarial Networks—0
A polar prediction model for learning to represent visual transformations—0
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs—0
Deep Embedding Clustering Driven by Sample Stability—0
Deep End-to-end Unsupervised Anomaly Detection—0
DeeperGCN: Training Deeper GCNs with Generalized Aggregation Functions—0
Deep Feature Gaussian Processes for Single-Scene Aerosol Optical Depth Reconstruction—0
Deep Feature Learning for Graphs—0
Deep Feature Learning for Wireless Spectrum Data—0
DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning—0
Policy Contrastive Imitation Learning—0
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies—0
Self Supervised Correlation-based Permutations for Multi-View Clustering—0
DeepGate3: Towards Scalable Circuit Representation Learning—0
Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation—0
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors—0
Deep Graph Generators: A Survey—0
Deep Graph Learning for Anomalous Citation Detection—0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network—0
Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery—0
DeepHEN: quantitative prediction essential lncRNA genes and rethinking essentialities of lncRNA genes—0
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture—0
Deep Hypergraph Structure Learning—0
Deep Hyperspherical Learning—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks—0
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs—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