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 3851–3900 of 10580 papers

TitleStatusHype
Generative Pretraining for Paraphrase Evaluation—0
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer—0
How Robust is Unsupervised Representation Learning to Distribution Shift?—0
Creating generalizable downstream graph models with random projections—0
Elastic Information Bottleneck—0
A Survey on Graph Representation Learning Methods—0
A Geometric Perspective on Optimal Representations for Reinforcement Learning—0
Eight challenges in developing theory of intelligence—0
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving—0
A Causal-based Framework for Multimodal Multivariate Time Series Validation Enhanced by Unsupervised Deep Learning as an Enabler for Industry 4.0—0
How to represent a word and predict it, too: Improving tied architectures for language modelling—0
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors—0
Cognitive Representation Learning of Self-Media Online Article Quality—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network—0
GEN Model: An Alternative Approach to Deep Neural Network Models—0
Cognitive maps and schizophrenia—0
GenURL: A General Framework for Unsupervised Representation Learning—0
How Fine-Tuning Allows for Effective Meta-Learning—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
How does the degree of novelty impacts semi-supervised representation learning for novel class retrieval?—0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
A Geometric Framework for Odor Representation—0
How Do Multilingual Encoders Learn Cross-lingual Representation?—0
Efficient Utilization of Large Pre-Trained Models for Low Resource ASR—0
A Survey on Graph-Based Deep Learning for Computational Histopathology—0
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators—0
Additional Positive Enables Better Representation Learning for Medical Images—0
CoGANPPIS: A Coevolution-enhanced Global Attention Neural Network for Protein-Protein Interaction Site Prediction—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
Geometric Disentanglement by Random Convex Polytopes—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—0
Geometric Relational Embeddings—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation—0
COD: Learning Conditional Invariant Representation for Domain Adaptation Regression—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Adversarial Defense Framework for Graph Neural Network—0
How Powerful is Implicit Denoising in Graph Neural Networks—0
HHTrack: Hyperspectral Object Tracking Using Hybrid Attention—0
Efficient Skill Discovery via Regret-Aware Optimization—0
Efficient Self-supervised Vision Transformers for Representation Learning—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—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