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 2876–2900 of 6661 papers

TitleStatusHype
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
Contrastive Learning of Global and Local Video Representations—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization—0
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise—0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—0
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer—0
Injecting Text in Self-Supervised Speech Pretraining—0
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits—0
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative Adversarial Learning via Kernel Density Discrimination—0
Heterogeneous bimodal attention fusion for speech emotion recognition—0
Contrastive Pre-training for Deep Session Data Understanding—0
Contrastive Learning of Features between Images and LiDAR—0
Boundary-Aware Proposal Generation Method for Temporal Action Localization—0
Exploiting Auxiliary Caption for Video Grounding—0
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text—0
Contrastive Learning of English Language and Crystal Graphs for Multimodal Representation of Materials Knowledge—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive 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