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 6001–6050 of 10580 papers

TitleStatusHype
Y-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning—0
Mathematics of Deep Learning—0
Deep Semi-supervised Learning with Double-Contrast of Features and Semantics—0
Improving Discriminative Visual Representation Learning via Automatic Mixup—0
Anisotropic Neural Representation Learning for High-Quality Neural Rendering—0
Matrix and Tensor Factorization Methods for Natural Language Processing—0
Improving Dense Contrastive Learning with Dense Negative Pairs—0
Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model—0
Improving deep forest by confidence screening—0
Improving Deep Embedded Clustering via Learning Cluster-level Representations—0
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality—0
Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation—0
Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures—0
Deep Self-representative Concept Factorization Network for Representation Learning—0
Max-Sliced Mutual Information—0
Multi-Frequency Information Enhanced Channel Attention Module for Speaker Representation Learning—0
DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction—0
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network—0
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering—0
Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning—0
Improving Clinical Predictions through Unsupervised Time Series Representation Learning—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
Bootstrap Your Own Correspondences—0
Adversarial Classifier for Imbalanced Problems—0
Measure Inducing Classification and Regression Trees for Functional Data—0
CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning—0
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition—0
Deep Residual Hashing—0
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences—0
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization—0
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing—0
Efficient Message Passing Architecture for GCN Training on HBM-based FPGAs with Orthogonal Topology On-Chip Networks—0
Deep Representation Learning with Part Loss for Person Re-Identification—0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
Better Pre-Training by Reducing Representation Confusion—0
Improve Supervised Representation Learning with Masked Image Modeling—0
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder—0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment—0
Improvements to Self-Supervised Representation Learning for Masked Image Modeling—0
Deep Representation Learning of Tissue Metabolome and Computed Tomography Images Annotates Non-invasive Classification and Prognosis Prediction of NSCLC—0
Multi-fidelity Stability for Graph Representation Learning—0
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review—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