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 5526–5550 of 6661 papers

TitleStatusHype
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction—0
Contrastive Learning for Local and Global Learning MRI Reconstruction—0
CRIS: CLIP-Driven Referring Image SegmentationCode1
Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations—0
Weakly-supervised Generative Adversarial Networks for medical image classification—0
Self-supervised Feature-Gate Coupling for Dynamic Network PruningCode0
A Simple Long-Tailed Recognition Baseline via Vision-Language ModelCode1
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive LearningCode1
UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic ArithmeticCode1
SimCLAD: A Simple Framework for Contrastive Learning of Acronym Disambiguation—0
Cross-Task Consistency Learning Framework for Multi-Task LearningCode0
ExCon: Explanation-driven Supervised Contrastive Learning for Image ClassificationCode1
Targeted Supervised Contrastive Learning for Long-Tailed RecognitionCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with GeneticsCode1
Contrastive Object-level Pre-training with Spatial Noise Curriculum LearningCode1
Simple Contrastive Representation Adversarial Learning for NLP Tasks—0
RegionCL: Can Simple Region Swapping Contribute to Contrastive Learning?Code1
SPCL: A New Framework for Domain Adaptive Semantic Segmentation via Semantic Prototype-based Contrastive LearningCode1
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster AssignmentCode0
MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning—0
Learning Representation for Clustering via Prototype Scattering and Positive SamplingCode1
S-SimCSE: Sampled Sub-networks for Contrastive Learning of Sentence Embedding—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