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

Showing 43514375 of 6661 papers

TitleStatusHype
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive AdaptationCode0
Supervised Contrastive Learning with Tree-Structured Parzen Estimator Bayesian Optimization for Imbalanced Tabular Data0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation0
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood LearningCode0
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell CheckingCode1
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment AnalysisCode1
MedCLIP: Contrastive Learning from Unpaired Medical Images and TextCode2
Universal hidden monotonic trend estimation with contrastive learning0
Unsupervised visualization of image datasets using contrastive learningCode1
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation0
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph TrainingCode0
Unifying Graph Contrastive Learning with Flexible Contextual ScopesCode1
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold0
Correlation between Alignment-Uniformity and Performance of Dense Contrastive RepresentationsCode0
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog0
Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent DiscoveryCode0
Weakly Supervised Face Naming with Symmetry-Enhanced Contrastive LossCode0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images0
Supervised Prototypical Contrastive Learning for Emotion Recognition in ConversationCode1
Non-Contrastive Learning Meets Language-Image Pre-TrainingCode0
Indoor Smartphone SLAM with Learned Echoic Location Features0
Towards Effective Image Manipulation Detection with Proposal Contrastive LearningCode1
Attention-Based Audio Embeddings for Query-by-ExampleCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified