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 5451–5500 of 10580 papers

TitleStatusHype
Breaking Down Word Semantics from Pre-trained Language Models through Layer-wise Dimension Selection—0
Learning Less-Overlapping Representations—0
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges—0
Incorporating Global Information in Local Attention for Knowledge Representation Learning—0
Incorporating GAN for Negative Sampling in Knowledge Representation Learning—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment—0
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection—0
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Adversarial Deep Learning in EEG Biometrics—0
Learning Object Permanence from Videos via Latent Imaginations—0
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning—0
Learning Monolingual Compositional Representations via Bilingual Supervision—0
Longitudinal Self-Supervised Learning—0
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection—0
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization—0
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm—0
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR—0
Do DALL-E and Flamingo Understand Each Other?—0
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens—0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Learning Natural Consistency Representation for Face Forgery Video Detection—0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI—0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
Learning Nuclei Representations with Masked Image Modelling—0
Incidence Networks for Geometric Deep Learning—0
In-bed Pressure-based Pose Estimation using Image Space Representation Learning—0
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation—0
Learning Online Data Association—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
Deep video representation learning: a survey—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
Long Short View Feature Decomposition via Contrastive Video Representation Learning—0
LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION—0
Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning—0
Brain-aligning of semantic vectors improves neural decoding of visual stimuli—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