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 10101–10150 of 10580 papers

TitleStatusHype
Random Forest Autoencoders for Guided Representation Learning—0
Gait Recognition via Disentangled Representation Learning—0
Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features—0
Semi-supervised multi-view concept decomposition—0
Gamma-Nets: Generalizing Value Estimation over Timescale—0
Gap Minimization for Knowledge Sharing and Transfer—0
GARF:Geometry-Aware Generalized Neural Radiance Field—0
GARLIC: GAussian Representation LearnIng for spaCe partitioning—0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—0
Gated Domain-Invariant Feature Disentanglement for Domain Generalizable Object Detection—0
GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training—0
Rank4Class: A Ranking Formulation for Multiclass Classification—0
RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistency—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Gaussian Masked Autoencoders—0
A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection—0
Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization—0
A Constituent-Centric Neural Architecture for Reading Comprehension—0
GCC: Generative Calibration Clustering—0
Self-supervised Learning: Generative or Contrastive—0
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GenDistiller: Distilling Pre-trained Language Models based on Generative Models—0
GeneCIS: A Benchmark for General Conditional Image Similarity—0
Gene finding revisited: improved robustness through structured decoding from learned embeddings—0
GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory—0
Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening—0
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology—0
RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels—0
General Item Representation Learning for Cold-start Content Recommendations—0
Generalizable Information Theoretic Causal Representation—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing—0
Generalizable task representation learning from human demonstration videos: a geometric approach—0
RankMI: A Mutual Information Maximizing Ranking Loss—0
Generalizable Zero-Shot Speaker Adaptive Speech Synthesis with Disentangled Representations—0
Generalization Analysis for Contrastive Representation Learning—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—0
Generalization Analysis for Deep Contrastive Representation Learning—0
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series—0
Generalization bound for estimating causal effects from observational network data—0
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage—0
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects—0
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior—0
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—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