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 4151–4200 of 10580 papers

TitleStatusHype
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training—0
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce—0
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Improving Robustness and Generality of NLP Models Using Disentangled Representations—0
Improving self-supervised representation learning via sequential adversarial masking—0
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling—0
Incidence Networks for Geometric Deep Learning—0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning—0
Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection—0
Improving Pixel-Level Contrastive Learning by Leveraging Exogenous Depth Information—0
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Active Multi-Task Representation Learning—0
EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization—0
ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning—0
A Structure-Aware Argument Encoder for Literature Discourse Analysis—0
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction—0
Improving One-Shot Learning through Fusing Side Information—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning—0
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
Generalized Supervised Contrastive Learning—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
Improving Optimization for Models With Continuous Symmetry Breaking—0
Improving Pseudo-label Training For End-to-end Speech Recognition Using Gradient Mask—0
dynnode2vec: Scalable Dynamic Network Embedding—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Improving Medical Visual Representation Learning with Pathological-level Cross-Modal Alignment and Correlation Exploration—0
A Generalized Model for Multidimensional Intransitivity—0
CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning—0
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding—0
Dynamic Spiking Framework for Graph Neural Networks—0
Dynamic Spectrum Matching with One-shot Learning—0
Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs—0
Improving Multi-Task Generalization via Regularizing Spurious Correlation—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks—0
Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks—0
Associative Learning Mechanism for Drug-Target Interaction Prediction—0
Improving Graph-Based Text Representations with Character and Word Level N-grams—0
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space—0
Show:102550
← PrevPage 84 of 212Next →
All datasetsSciDocsAnimals-10CIFAR10Circle DataSports10

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