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 5751–5800 of 10580 papers

TitleStatusHype
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
SPADE: Self-supervised Pretraining for Acoustic DisEntanglement—0
Disentanglement of Latent Representations via Causal InterventionsCode0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
Hyperbolic Contrastive Learning—0
Unpaired Multi-Domain Causal Representation Learning—0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms—0
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Graph Anomaly Detection in Time Series: A Survey—0
CRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement LearningCode0
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese NetworksCode0
Fairness and Accuracy under Domain GeneralizationCode0
Causality-based CTR Prediction using Graph Neural Networks—0
Overcoming Simplicity Bias in Deep Networks using a Feature Sieve—0
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy—0
The Influences of Color and Shape Features in Visual Contrastive Learning—0
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
Unbiased and Efficient Self-Supervised Incremental Contrastive LearningCode0
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans—0
HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction—0
Understanding Self-Supervised Pretraining with Part-Aware Representation LearningCode0
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding—0
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images—0
ERNet: Efficient and Reliable Human-Object Interaction DetectionCode0
Dual Box Embeddings for the Description Logic EL++Code0
Neural networks learn to magnify areas near decision boundariesCode0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance ModelCode0
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identificationCode0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Generative Slate Recommendation with Reinforcement Learning—0
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning—0
Which Features are Learned by CodeBert: An Empirical Study of the BERT-based Source Code Representation Learning—0
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
Score-based Causal Representation Learning with Interventions—0
Everything is Connected: Graph Neural Networks—0
DiME: Maximizing Mutual Information by a Difference of Matrix-Based EntropiesCode0
JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its ApplicationsCode0
Joint Representation Learning for Text and 3D Point CloudCode0
Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images—0
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition—0
Bayesian Models of Functional Connectomics and Behavior—0
EvoAAA: An evolutionary methodology for automated autoencoder architecture searchCode0
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss—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