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 4801–4850 of 10580 papers

TitleStatusHype
Cross-Lingual Word Representations: Induction and Evaluation—0
Ballroom Dance Movement Recognition Using a Smart Watch and Representation Learning—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
GLCC: A General Framework for Graph-Level Clustering—0
GLASS: GNN with Labeling Tricks for Subgraph Representation Learning—0
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments—0
Balancing Transferability and Discriminability for Unsupervised Domain Adaptation.—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval—0
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages—0
Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning—0
GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra—0
Cross-Lingual Relation Extraction with Transformers—0
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning—0
Balancing the Scales: Enhancing Fairness in Facial Expression Recognition with Latent Alignment—0
A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting—0
A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting—0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
Geometry of Deep Generative Models for Disentangled Representations—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation—0
Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs—0
Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning—0
Balance Regularized Neural Network Models for Causal Effect Estimation—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
Geometric Relational Embeddings—0
Multimodal learning with graphs—0
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Geometric Disentanglement by Random Convex Polytopes—0
Geometric Analysis of Nonconvex Optimization Landscapes for Overcomplete Learning—0
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion—0
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images—0
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
A Multi-Metric Latent Factor Model for Analyzing High-Dimensional and Sparse data—0
Additional Positive Enables Better Representation Learning for Medical Images—0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification—0
Geo-BERT Pre-training Model for Query Rewriting in POI Search—0
GenURL: A General Framework for Unsupervised Representation Learning—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