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 9551–9600 of 10580 papers

TitleStatusHype
Active Multi-Task Representation Learning—0
Effective Decoding in Graph Auto-Encoder using Triadic Closure—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
Effective Exploration Based on the Structural Information Principles—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Effective Transfer Learning for Low-Resource Natural Language Understanding—0
Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)—0
Active Multimodal Distillation for Few-shot Action Recognition—0
Progressive growing of self-organized hierarchical representations for exploration—0
Efficiency-oriented approaches for self-supervised speech representation learning—0
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Active metric learning and classification using similarity queries—0
Efficient Codebook and Factorization for Second Order Representation Learning—0
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Efficient Contextual Representation Learning With Continuous Outputs—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Efficient Deep Representation Learning by Adaptive Latent Space Sampling—0
Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Efficient Fairness-Performance Pareto Front Computation—0
Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model—0
Progressive Multi-Modal Fusion for Robust 3D Object Detection—0
Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods—0
Efficient Image Representation Learning with Federated Sampled Softmax—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
Efficient Large-Scale Visual Representation Learning And Evaluation—0
Efficient Learning of Domain-invariant Image Representations—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
Efficient Message Passing Architecture for GCN Training on HBM-based FPGAs with Orthogonal Topology On-Chip Networks—0
Efficient Model-Free Exploration in Low-Rank MDPs—0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
Efficient Multiscale Multimodal Bottleneck Transformer for Audio-Video Classification—0
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior—0
Efficient Planning with Latent Diffusion—0
Efficient Receptive Field Learning by Dynamic Gaussian Structure—0
Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals—0
Efficient Representation Learning via Adaptive Context Pooling—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition—0
Efficient Self-supervised Vision Transformers for Representation Learning—0
Efficient Skill Discovery via Regret-Aware Optimization—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution—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