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 10501–10550 of 10580 papers

TitleStatusHype
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio—0
RemoCap: Disentangled Representation Learning for Motion Capture—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation—0
Hierarchical Banzhaf Interaction for General Video-Language Representation Learning—0
Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning—0
Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning—0
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units—0
Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction—0
Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models—0
Remodeling Peptide-MHC-TCR Triad Binding as Sequence Fusion for Immunogenicity Prediction—0
Hierarchical Convolutional Attention Networks for Text Classification—0
Hierarchical Correlation Clustering and Tree Preserving Embedding—0
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training—0
Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition—0
Hierarchical discriminative learning improves visual representations of biomedical microscopy—0
Hierarchical disentangled representation learning for singing voice conversion—0
Hierarchical Disentangled Representation Learning for Outdoor Illumination Estimation and Editing—0
Hierarchical Disentangle Network for Object Representation Learning—0
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions—0
Self-Supervised Policy Adaptation—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
TARA: Training and Representation Alteration for AI Fairness and Domain Generalization—0
RENs: Relevance Encoding Networks—0
RepAL: A Simple and Plug-and-play Method for Improving Unsupervised Sentence Representations—0
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation—0
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA—0
Hierarchically Clustered Representation Learning—0
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning—0
Hierarchically Robust Representation Learning—0
RepCL: Exploring Effective Representation for Continual Text Classification—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Hierarchical Network Fusion for Multi-Modal Electron Micrograph Representation Learning with Foundational Large Language Models—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
Hierarchical Point Cloud Encoding and Decoding with Lightweight Self-Attention based Model—0
Hierarchical Pretraining for Biomedical Term Embeddings—0
Hierarchical Prototype Network for Continual Graph Representation Learning—0
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
Hierarchical Query Classification in E-commerce Search—0
Hierarchical Representation Learning for Kinship Verification—0
Hierarchical Representation Learning for Markov Decision Processes—0
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training—0
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation—0
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Hierarchical Sparse Coding With Geometric Prior For Visual Geo-Location—0
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation—0
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images—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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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