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 9351–9400 of 10580 papers

TitleStatusHype
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models—0
Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels—0
Preventing Posterior Collapse with delta-VAEs—0
Distribution Preserving Graph Representation Learning—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
div2vec: Diversity-Emphasized Node Embedding—0
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection—0
Diversifying Joint Vision-Language Tokenization Learning—0
Divide and Conquer Self-Supervised Learning for High-Content Imaging—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning—0
DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction—0
AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler—0
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets—0
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
Recommendation with Attribute-aware Product Networks: A Representation Learning Model—0
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning—0
Semi Supervised Meta Learning for Spatiotemporal Learning—0
Document Representation Learning for Patient History Visualization—0
Do DALL-E and Flamingo Understand Each Other?—0
Principled Representation Learning for Entity Alignment—0
Does Corpus Quality Really Matter for Low-Resource Languages?—0
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation—0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
Does It Happen? Multi-hop Path Structures for Event Factuality Prediction with Graph Transformer Networks—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning—0
Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers—0
Prior Learning in Introspective VAEs—0
Self-supervised Graph Neural Network for Mechanical CAD Retrieval—0
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy—0
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Privacy-Aware Adversarial Network in Human Mobility Prediction—0
Domain Adaptive Graph Classification—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Privacy-Aware Human Mobility Prediction via Adversarial Networks—0
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy—0
Domain-Agnostic Clustering with Self-Distillation—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