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 6701–6750 of 10580 papers

TitleStatusHype
Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity—0
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks—0
Learning Sparse Representations in Reinforcement Learning with Sparse Coding—0
Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex—0
Chat Discrimination for Intelligent Conversational Agents with a Hybrid CNN-LMTGRU Network—0
ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency—0
Neural-Kernelized Conditional Density Estimation—0
Affinity-VAE: incorporating prior knowledge in representation learning from scientific images—0
Active Discriminative Text Representation Learning—0
Neural Knitworks: Patched Neural Implicit Representation Networks—0
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck—0
Learning sound representations using trainable COPE feature extractors—0
Domain Representation for Knowledge Graph Embedding—0
Learning Solving Procedure for Artificial Neural Network—0
Learning Smooth and Fair Representations—0
Representation Learning on Visual-Symbolic Graphs for Video Understanding—0
Towards Unsupervised Domain Generalization—0
Learning Shape Representations for Clothing Variations in Person Re-Identification—0
Domain-Invariant Representation Learning with Global and Local Consistency—0
Chart Auto-Encoders for Manifold Structured Data—0
Learning Semantics: An Opportunity for Effective 6G Communications—0
Learning Semantic Relatedness in Community Question Answering Using Neural Models—0
Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing—0
Learning Robust Visual-Semantic Embeddings—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
Characters or Morphemes: How to Represent Words?—0
A Scalable Technique for Weak-Supervised Learning with Domain Constraints—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning Robust Representations with Graph Denoising Policy Network—0
Neural Oscillators are Universal—0
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Neural Physicist: Learning Physical Dynamics from Image Sequences—0
Learning Robust Representations for Computer Vision—0
Neural Predictive Belief Representations—0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
Learning Robust and Multilingual Speech Representations—0
Domain Generalization -- A Causal Perspective—0
Neural Speech Embeddings for Speech Synthesis Based on Deep Generative Networks—0
A Scalable and Effective Alternative to Graph Transformers—0
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis—0
Learning Retrospective Knowledge with Reverse Reinforcement Learning—0
Domain-aware Self-supervised Pre-training for Weakly-supervised Meme Analysis—0
Neural Thermodynamics I: Entropic Forces in Deep and Universal Representation Learning—0
Learning Representations Using Complex-Valued Nets—0
Learning Representations Robust to Group Shifts and Adversarial Examples—0
Domain Aligned CLIP for Few-shot Classification—0
Domain-Agnostic Prior for Transfer Semantic Segmentation—0
NeuroNURBS: Learning Efficient Surface Representations for 3D Solids—0
Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer—0
A Sample Complexity Separation between Non-Convex and Convex Meta-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