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 3376–3400 of 6661 papers

TitleStatusHype
Event-Centric Query Expansion in Web Search—0
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses—0
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play—0
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural NetworksCode1
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation—0
Controlled Text Generation with Hidden Representation TransformationsCode0
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion PriorsCode2
W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition—0
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-TuningCode1
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER—0
Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining—0
Whitening-based Contrastive Learning of Sentence EmbeddingsCode1
Point Cloud Completion Guided by Prior Knowledge via Causal Inference—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Matrix Information Theory for Self-Supervised LearningCode1
Three Towers: Flexible Contrastive Learning with Pretrained Image Models—0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Towards Open-World Segmentation of PartsCode1
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly DetectionCode1
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning—0
Hierarchical Verbalizer for Few-Shot Hierarchical Text ClassificationCode1
RankCSE: Unsupervised Sentence Representations Learning via Learning to RankCode1
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance LearningCode0
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression—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