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 5001–5025 of 6661 papers

TitleStatusHype
Generalization Analysis for Deep Contrastive Representation Learning—0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
Generalization Bounds for Adversarial Contrastive Learning—0
Generalized Class Discovery in Instance Segmentation—0
Generalizing Graph ODE for Learning Complex System Dynamics across Environments—0
Generalizing Supervised Contrastive learning: A Projection Perspective—0
General-Purpose Multi-Modal OOD Detection Framework—0
Generating Compositional Color Representations from Text—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text—0
Exploiting Auxiliary Caption for Video Grounding—0
Generative Adversarial Learning via Kernel Density Discrimination—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits—0
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
GenURL: A General Framework for Unsupervised Representation Learning—0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—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