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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3901–3950 of 10580 papers

TitleStatusHype
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Speech Command Recognition Leveraging Spiking Neural Network and Curriculum Learning-based Knowledge Distillation—0
COD: Learning Conditional Invariant Representation for Domain Adaptation Regression—0
Adversarial Defense Framework for Graph Neural Network—0
HYDEN: Hyperbolic Density Representations for Medical Images and Reports—0
Efficient Skill Discovery via Regret-Aware Optimization—0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
Efficient Self-supervised Vision Transformers for Representation Learning—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding—0
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild—0
A Survey on Extraction of Causal Relations from Natural Language Text—0
GIQ: Benchmarking 3D Geometric Reasoning of Vision Foundation Models with Simulated and Real Polyhedra—0
Efficient Representation Learning via Adaptive Context Pooling—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
GLAD: Global-Local-Alignment Descriptor for Pedestrian Retrieval—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals—0
Code Representation Learning with Prüfer Sequences—0
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
Global-Aware Monocular Semantic Scene Completion with State Space Models—0
A Survey on Concept Factorization: From Shallow to Deep Representation Learning—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μP Parametrization—0
Global Interaction Modelling in Vision Transformer via Super Tokens—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Code Representation Learning At Scale—0
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition—0
Global-Locally Self-Attentive Encoder for Dialogue State Tracking—0
Cross-Modal Alignment Learning of Vision-Language Conceptual Systems—0
Global Optimality in Neural Network Training—0
Efficient Receptive Field Learning by Dynamic Gaussian Structure—0
Efficient Planning with Latent Diffusion—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior—0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning—0
Efficient Multiscale Multimodal Bottleneck Transformer for Audio-Video Classification—0
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training—0
Cross-modal Common Representation Learning by Hybrid Transfer Network—0
Hybrid deep learning methods for phenotype prediction from clinical notes—0
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners—0
GNEG: Graph-Based Negative Sampling for word2vec—0
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs—0
Cross-Modal Discrete Representation Learning—0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—0
Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs—0
Efficient Model-Free Exploration in Low-Rank MDPs—0
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—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