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 5701–5750 of 10580 papers

TitleStatusHype
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Length- and Noise-aware Training Techniques for Short-utterance Speaker Recognition—0
Improving Subgraph Representation Learning via Multi-View Augmentation—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
Less can be more in contrastive learning—0
Deep tree-ensembles for multi-output prediction—0
Less Data, More Knowledge: Building Next Generation Semantic Communication Networks—0
Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach—0
Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps—0
DeepTrax: Embedding Graphs of Financial Transactions—0
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping—0
Deep Trans-layer Unsupervised Networks for Representation Learning—0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI—0
Leveraging Color Channel Independence for Improved Unsupervised Object Detection—0
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection—0
Leveraging Herpangina Data to Enhance Hospital-level Prediction of Hand-Foot-and-Mouth Disease Admissions Using UPTST—0
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection—0
Improving self-supervised representation learning via sequential adversarial masking—0
Leveraging large language models for efficient representation learning for entity resolution—0
Leveraging Latent Representations of Speech for Indian Language Identification—0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—0
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition—0
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
Meta-Path-Free Representation Learning on Heterogeneous Networks—0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks—0
Towards Achieving Perfect Multimodal Alignment—0
Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations—0
Meta Representation Learning with Contextual Linear Bandits—0
Leveraging sparse and shared feature activations for disentangled representation learning—0
Sparsity regularization via tree-structured environments for disentangled representations—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Improving Robustness and Generality of NLP Models Using Disentangled Representations—0
Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation—0
Lexical Manifold Reconfiguration in Large Language Models: A Novel Architectural Approach for Contextual Modulation—0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—0
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation—0
LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition—0
STELLA: Continual Audio-Video Pre-training with Spatio-Temporal Localized Alignment—0
Dual Space Graph Contrastive Learning—0
Lifelong Knowledge-Enriched Social Event Representation Learning—0
Lifelong Learning of Hate Speech Classification on Social Media—0
Deep Temporal Contrastive Clustering—0
Lifestyle-Informed Personalized Blood Biomarker Prediction via Novel Representation Learning—0
Lifted Rule Injection for Relation Embeddings—0
Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs—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