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 12511275 of 6661 papers

TitleStatusHype
New Intent Discovery with Pre-training and Contrastive LearningCode1
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
A graph-transformer for whole slide image classificationCode1
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation ExtractionCode1
Free Lunch for Surgical Video Understanding by Distilling Self-SupervisionsCode1
Masked Image Modeling with Denoising ContrastCode1
RankGen: Improving Text Generation with Large Ranking ModelsCode1
Domain Enhanced Arbitrary Image Style Transfer via Contrastive LearningCode1
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive SummarizationCode1
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and IsotropizationCode1
Near out-of-distribution detection for low-resolution radar micro-Doppler signaturesCode1
Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked AutoencoderCode1
Learning Non-target Knowledge for Few-shot Semantic SegmentationCode1
EASE: Entity-Aware Contrastive Learning of Sentence EmbeddingCode1
Cross-modal Contrastive Learning for Speech TranslationCode1
A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationCode1
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive LearningCode1
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation ExtractionCode1
Contrastive Learning for Prompt-Based Few-Shot Language LearnersCode1
Debiased Contrastive Learning of Unsupervised Sentence RepresentationsCode1
Contrastive Learning for Improving ASR Robustness in Spoken Language UnderstandingCode1
Knowledge Graph Contrastive Learning for RecommendationCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little CostCode1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive LearningCode1
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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