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 6651–6700 of 10580 papers

TitleStatusHype
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing—0
Understanding Metric Learning on Unit Hypersphere and Generating Better Examples for Adversarial Training—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
Learning Temporally Latent Causal Processes from General Temporal DataCode1
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning—0
PASS: Patch-Aware Self-Supervision for Vision Transformer—0
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations—0
Distributional Decision Transformer for Hindsight Information Matching—0
Provably Filtering Exogenous Distractors using Multistep Inverse Dynamics—0
Disentangling Properties of Contrastive Methods—0
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representation—0
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces—0
Vi-MIX FOR SELF-SUPERVISED VIDEO REPRESENTATION—0
Shaping latent representations using Self-Organizing Maps with Relevance Learning—0
Invariant Causal Representation Learning for Out-of-Distribution Generalization—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Training Deep Generative Models via Auxiliary Supervised Learning—0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
Improving Discriminative Visual Representation Learning via Automatic Mixup—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Neural Knitworks: Patched Neural Implicit Representation Networks—0
Distribution Knowledge Embedding for Graph PoolingCode1
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigationCode0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
Exploratory State Representation LearningCode0
Making Curiosity Explicit in Vision-based RL—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
Audio-to-Image Cross-Modal Generation—0
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial ImagesCode0
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization—0
ICPE: An Item Cluster-Wise Pareto-Efficient Framework for Recommendation Debiasing—0
Self-Supervised Video Representation Learning by Video Incoherence Detection—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates—0
Self-Enhancing Multi-filter Sequence-to-Sequence Model—0
Scalable deeper graph neural networks for high-performance materials property predictionCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
High-Dimensional Discrete Bayesian Optimization with Self-Supervised Representation Learning for Data-Efficient Materials ExplorationCode0
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Edge but not Least: Cross-View Graph Pooling—0
AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses—0
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential RecommendationCode1
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation—0
Dense Contrastive Visual-Linguistic Pretraining—0
Holistic Semi-Supervised Approaches for EEG Representation Learning—0
Long Short View Feature Decomposition via Contrastive Video Representation Learning—0
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?Code0
Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial 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