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 4101–4150 of 10580 papers

TitleStatusHype
Choose What You Need: Disentangled Representation Learning for Scene Text Recognition, Removal and Editing—0
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancerCode0
The Curse of Diversity in Ensemble-Based ExplorationCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
Statistical Edge Detection And UDF Learning For Shape Representation—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-TrainingCode0
Unsupervised machine learning for data-driven rock mass classification: addressing limitations in existing systems using drilling data—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units—0
A Mutual Information Perspective on Federated Contrastive Learning—0
SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery—0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments—0
SoMeR: Multi-View User Representation Learning for Social Media—0
What Makes for Good Image Captions?—0
Locality Regularized Reconstruction: Structured Sparsity and Delaunay TriangulationsCode0
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation—0
Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment—0
Weighted Point Cloud Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric—0
Protein Representation Learning by Capturing Protein Sequence-Structure-Function Relationship—0
ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference OptimizationCode0
Embedded Representation Learning Network for Animating Styled Video Portrait—0
SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set RetrievalCode0
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual LearningCode0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
TabVFL: Improving Latent Representation in Vertical Federated Learning—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation—0
VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition—0
PromptCL: Improving Event Representation via Prompt Template and Contrastive LearningCode0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks—0
Sparse Reconstruction of Optical Doppler Tomography with Alternative State Space Model and Attention—0
Self-supervised visual learning in the low-data regime: a comparative evaluation—0
Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect EstimationCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for VisionCode0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—0
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models—0
Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce Search—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks—0
Machine Learning Techniques for MRI Data Processing at Expanding Scale—0
Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs—0
General Item Representation Learning for Cold-start Content Recommendations—0
PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity Animatable Human Reconstruction—0
Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal 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