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 6601–6650 of 10580 papers

TitleStatusHype
dpVAEs: Fixing Sample Generation for Regularized VAEs—0
Learning to Detect: A Data-driven Approach for Network Intrusion Detection—0
Learning to Control Latent Representations for Few-Shot Learning of Named Entities—0
MuSiCNet: A Gradual Coarse-to-Fine Framework for Irregularly Sampled Multivariate Time Series Analysis—0
Learning to Compress: Local Rank and Information Compression in Deep Neural Networks—0
DPGIIL: Dirichlet Process-Deep Generative Model-Integrated Incremental Learning for Clustering in Transmissibility-based Online Structural Anomaly Detection—0
CH-Go: Online Go System Based on Chunk Data Storage—0
A self-supervised framework for learning whole slide representations—0
A Flexible Framework for Discovering Novel Categories with Contrastive Learning—0
Learning to Ask: Conversational Product Search via Representation Learning—0
Learning to Align Sequential Actions in the Wild—0
Mutual Information Minimization Based Disentangled Learning Framework For Causal Effect Estimation—0
DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos—0
MVC: A Multi-Task Vision Transformer Network for COVID-19 Diagnosis from Chest X-ray Images—0
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification—0
Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization—0
CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection—0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation—0
Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations—0
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving—0
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning—0
Do We Still Need Automatic Speech Recognition for Spoken Language Understanding?—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Learning Text Pair Similarity with Context-sensitive Autoencoders—0
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?—0
A Self-supervised Approach for Semantic Indexing in the Context of COVID-19 Pandemic—0
AF-KAN: Activation Function-Based Kolmogorov-Arnold Networks for Efficient Representation Learning—0
Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition—0
Learning telic-controllable state representations—0
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation—0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Chemical Property Prediction Under Experimental Biases—0
Natural Language Inference with Definition Embedding Considering Context On the Fly—0
Natural Language Supervision for Low-light Image Enhancement—0
Navigating the Dynamics of Financial Embeddings over Time—0
Learning Task-Relevant Features via Contrastive Input Morphing—0
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
Learning Target-aware Representation for Visual Tracking via Informative Interactions—0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations—0
Learning Successor Features with Distributed Hebbian Temporal Memory—0
NECA: Network-Embedded Deep Representation Learning for Categorical Data—0
Learning Subgoal Representations with Slow Dynamics—0
Do Trajectories Encode Verb Meaning?—0
Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking—0
Learning Structured Representations of Visual Scenes—0
Negative Sampling for Contrastive Representation Learning: A Review—0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—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