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 8851–8900 of 10580 papers

TitleStatusHype
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services—0
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning—0
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning—0
CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Network—0
CTRL-O: Language-Controllable Object-Centric Visual Representation Learning—0
CultureMERT: Continual Pre-Training for Cross-Cultural Music Representation Learning—0
CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis—0
CURL: Co-trained Unsupervised Representation Learning for Image Classification—0
Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning—0
Self-supervised Contrastive Attributed Graph Clustering—0
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment—0
Point Cloud Learning with Transformer—0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning—0
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss—0
Cycle Consistency Driven Object Discovery—0
Cycle-Contrast for Self-Supervised Video Representation Learning—0
Cyclic Refiner: Object-Aware Temporal Representation Learning for Multi-View 3D Detection and Tracking—0
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering—0
CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING—0
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding—0
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
DALG: Deep Attentive Local and Global Modeling for Image Retrieval—0
DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning—0
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
DARI: Distance metric And Representation Integration for Person Verification—0
DAS-MAE: A self-supervised pre-training framework for universal and high-performance representation learning of distributed fiber-optic acoustic sensing—0
Semantically Consistent Multi-view Representation Learning—0
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding—0
Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos—0
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
Data Dimension Reduction makes ML Algorithms efficient—0
Data-Driven Offline Decision-Making via Invariant Representation Learning—0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis—0
DAViD: Domain Adaptive Visually-Rich Document Understanding with Synthetic Insights—0
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Multi-local Collaborative AutoEncoder—0
De^2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation—0
DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification—0
Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Self-Supervised Contrastive Pre-Training for Multivariate Point Processes—0
De-biased Representation Learning for Fairness with Unreliable Labels—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