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 6001–6050 of 10580 papers

TitleStatusHype
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment—0
Mathematics of Deep Learning—0
M^2Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction—0
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP—0
LVLM-empowered Multi-modal Representation Learning for Visual Place Recognition—0
ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning—0
Longitudinal Self-Supervised Learning—0
Matrix Factorization with Dynamic Multi-view Clustering for Recommender System—0
Eccentric Regularization: Minimizing Hyperspherical Energy without explicit projection—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning—0
LRHP: Learning Representations for Human Preferences via Preference Pairs—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Incremental Few-Shot Object Detection for Robotics—0
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing—0
Low-Rank MDPs with Continuous Action Spaces—0
EASE: Embodied Active Event Perception via Self-Supervised Energy Minimization—0
Clustered FedStack: Intermediate Global Models with Bayesian Information Criterion—0
ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models—0
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding—0
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces—0
Low Dimensional State Representation Learning with Reward-shaped Priors—0
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics—0
Lossy compression of statistical data using quantum annealer—0
MDL-CW: A Multimodal Deep Learning Framework With Cross Weights—0
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation—0
Loss is its own Reward: Self-Supervision for Reinforcement Learning—0
A Structure-Aware Argument Encoder for Literature Discourse Analysis—0
HiCOMEX: Facial Action Unit Recognition Based on Hierarchy Intensity Distribution and COMEX Relation Learning—0
Lorentzian Distance Learning—0
Learning Object Permanence from Videos via Latent Imaginations—0
Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning—0
Look, Learn and Leverage (L^3): Mitigating Visual-Domain Shift and Discovering Intrinsic Relations via Symbolic Alignment—0
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Generalized Supervised Contrastive Learning—0
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
Long Short View Feature Decomposition via Contrastive Video Representation Learning—0
Long-horizon video prediction using a dynamic latent hierarchy—0
LoNe Sampler: Graph node embeddings by coordinated local neighborhood sampling—0
Dynamic Spiking Framework for Graph Neural Networks—0
Logographic Information Aids Learning Better Representations for Natural Language Inference—0
Dynamic Spectrum Matching with One-shot 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