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 28012825 of 6661 papers

TitleStatusHype
Joint Searching and Grounding: Multi-Granularity Video Content RetrievalCode0
PE-GAN: Prior Embedding GAN for PXD images at Belle IICode0
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest NeighborsCode0
Graph Diffusion Network for Drug-Gene PredictionCode0
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive LearningCode0
Joint Masked Reconstruction and Contrastive Learning for Mining Interactions Between ProteinsCode0
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic DecompositionCode0
Joint Representation Learning for Text and 3D Point CloudCode0
JTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence EmbeddingsCode0
Knowing Where and What: Unified Word Block Pretraining for Document UnderstandingCode0
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity RecognitionCode0
Balancing Embedding Spectrum for RecommendationCode0
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Continual Learning: Less Forgetting, More OOD Generalization via Adaptive Contrastive ReplayCode0
JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model EditsCode0
Enhancing pretraining efficiency for medical image segmentation via transferability metricsCode0
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label LearningCode0
Continual Graph Convolutional Network for Text ClassificationCode0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Balanced Multi-Relational Graph ClusteringCode0
Enhancing Multivariate Time Series-based Solar Flare Prediction with Multifaceted Preprocessing and Contrastive LearningCode0
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive LearningCode0
Continual Contrastive Learning for Image ClassificationCode0
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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