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 3776–3800 of 6661 papers

TitleStatusHype
A Semi-Supervised Classification Method of Apicomplexan Parasites and Host Cell Using Contrastive Learning Strategy—0
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images—0
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings—0
AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning—0
A Siamese Network to Detect If Two Iris Images Are Monozygotic—0
A Simple Baseline for Weakly-Supervised Scene Graph Generation—0
A Simple Contrastive Framework Of Item Tokenization For Generative Recommendation—0
A simple framework for contrastive learning phases of matter—0
A Simple Framework for Uncertainty in Contrastive Learning—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
A Simplified Framework for Contrastive Learning for Node Representations—0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—0
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification—0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series—0
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
A Survey on Contrastive Self-supervised Learning—0
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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