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 2601–2625 of 6661 papers

TitleStatusHype
Long-Tail Learning with Rebalanced Contrastive Loss—0
Local-Global History-aware Contrastive Learning for Temporal Knowledge Graph Reasoning—0
Guarding Barlow Twins Against Overfitting with Mixed SamplesCode1
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency—0
Just-in-Time Detection of Silent Security Patches—0
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image CaptioningCode1
Hypergraph Contrastive Learning for Drug Trafficking Community DetectionCode1
Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition—0
A Generalizable Deep Learning System for Cardiac MRICode0
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
Spectral Temporal Contrastive Learning—0
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation LearningCode1
Text Attribute Control via Closed-Loop Disentanglement—0
Manipulating the Label Space for In-Context Classification—0
Optimal Sample Complexity of Contrastive Learning—0
Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence PairsCode0
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation—0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks—0
Unveiling Vulnerabilities of Contrastive Recommender Systems to Poisoning AttacksCode1
Contrastive Denoising Score for Text-guided Latent Diffusion Image EditingCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
DSS: Synthesizing long Digital Ink using Data augmentation, Style encoding and Split generation—0
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
SenTest: Evaluating Robustness of Sentence Encoders—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