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 4751–4800 of 10580 papers

TitleStatusHype
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
A Closer Look at Personalization in Federated Image Classification—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
Learning Structurally Stabilized Representations for Multi-modal Lossless DNA Storage—0
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection—0
Gradients as Features for Deep Representation Learning—0
Cross-modal Retrieval with Improved Graph Convolution—0
Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning—0
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation—0
GRADE: Graph Dynamic Embedding—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
Cross-modal Representation Learning for Zero-shot Action Recognition—0
A Mutual Information Perspective on Federated Contrastive Learning—0
GPU Activity Prediction using Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis—0
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks—0
Effective Latent Differential Equation Models via Attention and Multiple Shooting—0
Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images—0
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks—0
A Mutual Information Maximization Perspective of Language Representation Learning—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification—0
Cross-Modal Discrete Representation Learning—0
GNEG: Graph-Based Negative Sampling for word2vec—0
Cross-Modal Contrastive Representation Learning for Audio-to-Image Generation—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
Cross-modal Common Representation Learning by Hybrid Transfer Network—0
GLProtein: Global-and-Local Structure Aware Protein Representation Learning—0
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition—0
Batch Curation for Unsupervised Contrastive Representation Learning—0
A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring—0
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms—0
Global Optimality in Neural Network Training—0
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning—0
Global-Locally Self-Attentive Encoder for Dialogue State Tracking—0
Cross-Modal Alignment Learning of Vision-Language Conceptual Systems—0
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition—0
Cross-Modal 3D Representation with Multi-View Images and Point Clouds—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Global Interaction Modelling in Vision Transformer via Super Tokens—0
Cross-media Similarity Metric Learning with Unified Deep Networks—0
Banyan: Improved Representation Learning with Explicit Structure—0
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis—0
Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μP Parametrization—0
Global-Aware Monocular Semantic Scene Completion with State Space 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