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 7151–7200 of 10580 papers

TitleStatusHype
Learning Geospatial Region Embedding with Heterogeneous Graph—0
div2vec: Diversity-Emphasized Node Embedding—0
Learning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network—0
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images—0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes—0
A Review on Deep Learning Techniques for Video Prediction—0
AEMIM: Adversarial Examples Meet Masked Image Modeling—0
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
Distribution Preserving Graph Representation Learning—0
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance—0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training—0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications—0
A Review of Text Style Transfer using Deep Learning—0
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency—0
Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks—0
Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning—0
Learning from Streaming Video with Orthogonal Gradients—0
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification—0
Learning from Multiview Correlations in Open-Domain Videos—0
Persistent Homology and Graphs Representation Learning—0
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations—0
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification—0
A Review of Mechanistic Models of Event Comprehension—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
AE2-Nets: Autoencoder in Autoencoder Networks—0
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning—0
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation—0
Distributional Decision Transformer for Hindsight Information Matching—0
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning—0
Learning for Counterfactual Fairness from Observational Data—0
Distributed Word Representation Learning for Cross-Lingual Dependency Parsing—0
Learning Flexible Visual Representations via Interactive Gameplay—0
Learning First-Order Symbolic Representations for Planning from the Structure of the State Space—0
Distributed Variational Representation Learning—0
Learning finite-dimensional coding schemes with nonlinear reconstruction maps—0
Gaze Prediction as a Function of Eye Movement Type and Individual Differences—0
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning—0
Perturbation Ontology based Graph Attention Networks—0
Decentralized Unsupervised Learning of Visual Representations—0
PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity Animatable Human Reconstruction—0
CCFace: Classification Consistency for Low-Resolution Face Recognition—0
Distributed Representations of Geographically Situated Language—0
Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP—0
Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification—0
Distributed Representations for Compositional Semantics—0
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection—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