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 7951–8000 of 10580 papers

TitleStatusHype
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model—0
Beyond Pairwise Correlations: Higher-Order Redundancies in Self-Supervised Representation Learning—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Modeling Complex Dependencies for Session-based Recommendations via Graph Neural Networks—0
Rethinking Alignment and Uniformity in Unsupervised Semantic Segmentation—0
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning—0
Robust contrastive learning and nonlinear ICA in the presence of outliers—0
Robust Graph Structure Learning under Heterophily—0
Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay—0
Rethinking Fair Representation Learning for Performance-Sensitive Tasks—0
Robust Representation Learning for Unified Online Top-K Recommendation—0
Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud—0
Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis—0
Hard Negative Sampling Strategies for Contrastive Representation Learning—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation—0
Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion—0
Input-independent Attention Weights Are Expressive Enough: A Study of Attention in Self-supervised Audio Transformers—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Robust Anchor Embedding for Unsupervised Video Person Re-Identification in the Wild—0
Graph AI in Medicine—0
Rethinking Positive Pairs in Contrastive Learning—0
Rethinking Robust Representation Learning Under Fine-grained Noisy Faces—0
Rethinking Self-Supervised Learning Within the Framework of Partial Information Decomposition—0
Rethinking Self-Supervised Visual Representation Learning in Pre-training for 3D Human Pose and Shape Estimation—0
Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability—0
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning—0
Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder—0
HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning—0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network—0
H^3GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning—0
RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection—0
Retrieval-Augmented Egocentric Video Captioning—0
Retrieval-based Disentangled Representation Learning with Natural Language Supervision—0
Decomposing Mutual Information for Representation Learning—0
Retrieval of Scientific and Technological Resources for Experts and Scholars—0
GWPT: A Green Word-Embedding-based POS Tagger—0
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
RETRO: REthinking Tactile Representation Learning with Material PriOrs—0
Return-Based Contrastive Representation Learning for Reinforcement Learning—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning—0
Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets—0
Robust and Controllable Object-Centric Learning through Energy-based Models—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