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 7451–7500 of 10580 papers

TitleStatusHype
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Progressive Multi-Modal Fusion for Robust 3D Object Detection—0
Recurring the Transformer for Video Action Recognition—0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models—0
Hierarchical Uncertainty-Aware Graph Neural Network—0
Recognition Method of Important Words in Korean Text based on Reinforcement Learning—0
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—0
Controlling Computation versus Quality for Neural Sequence Models—0
Hierarchical Transformer for Scalable Graph Learning—0
Understanding Deep Contrastive Learning via Coordinate-wise Optimization—0
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations—0
PSCodec: A Series of High-Fidelity Low-bitrate Neural Speech Codecs Leveraging Prompt Encoders—0
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning—0
Recognize Actions by Disentangling Components of Dynamics—0
Bilingual Distributed Word Representations from Document-Aligned Comparable Data—0
CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding—0
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation—0
Deep Contextual Recurrent Residual Networks for Scene Labeling—0
Recommendations by Concise User Profiles from Review Text—0
Prompt Learning on Temporal Interaction Graphs—0
Prompt-Matched Semantic Segmentation—0
Bilinear Supervised Hashing Based on 2D Image Features—0
Deep Concept Identification for Generative Design—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
Hierarchical Sparse Coding With Geometric Prior For Visual Geo-Location—0
Recent Advances in Autoencoder-Based Representation Learning—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation—0
Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech—0
Protecting gender and identity with disentangled speech representations—0
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction—0
Protein-ligand binding representation learning from fine-grained interactions—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
A Comparison of Discrete Latent Variable Models for Speech Representation Learning—0
DeepCodeProbe: Towards Understanding What Models Trained on Code Learn—0
Prototype Memory for Large-scale Face Representation Learning—0
Hierarchical Representation Learning for Markov Decision Processes—0
BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models—0
Hierarchical Representation Learning for Kinship Verification—0
Hierarchical Query Classification in E-commerce Search—0
BIGSAGE: unsupervised inductive representation learning of graph via bi-attended sampling and global-biased aggregating—0
Reasoning over Multi-view Knowledge Graphs—0
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
Hierarchical Prototype Network for Continual Graph 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