SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 4501–4550 of 6661 papers

TitleStatusHype
Manifold Contrastive Learning with Variational Lie Group OperatorsCode0
Online Unsupervised Video Object Segmentation via Contrastive Motion ClusteringCode0
What Constitutes Good Contrastive Learning in Time-Series Forecasting?Code0
Contrastive Disentangled Learning on Graph for Node Classification—0
Understanding Contrastive Learning Through the Lens of Margins—0
Deep Double Self-Expressive Subspace ClusteringCode0
M3PT: A Multi-Modal Model for POI Tagging—0
Crowdsourcing and Evaluating Text-Based Audio Retrieval RelevancesCode0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings—0
Description-Enhanced Label Embedding Contrastive Learning for Text ClassificationCode0
Learning on Graphs under Label Noise—0
Fine-Tuned but Zero-Shot 3D Shape Sketch View Similarity and Retrieval—0
Enhanced Multimodal Representation Learning with Cross-modal KD—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient EncoderCode0
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction—0
Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework—0
Liquidity takers behavior representation through a contrastive learning approach—0
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression ValuesCode0
Regularizing with Pseudo-Negatives for Continual Self-Supervised LearningCode0
A brief review of contrastive learning applied to astrophysics—0
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point CloudCode0
Contrastive Representation Disentanglement for Clustering—0
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce—0
Automatic retrieval of corresponding US views in longitudinal examinationsCode0
Rethinking Weak Supervision in Helping Contrastive Learning—0
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement—0
Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive LearningCode0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function—0
Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training—0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
Subgraph Networks Based Contrastive Learning—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage—0
YONA: You Only Need One Adjacent Reference-frame for Accurate and Fast Video Polyp DetectionCode0
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives—0
MoviePuzzle: Visual Narrative Reasoning through Multimodal Order Learning—0
Oversmoothing: A Nightmare for Graph Contrastive Learning?Code0
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs—0
Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints—0
Enhancing the Unified Streaming and Non-streaming Model with Contrastive Learning—0
CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI ReconstructionCode0
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance DetectionCode0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified