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 3751–3775 of 6661 papers

TitleStatusHype
A Novel Approach to for Multimodal Emotion Recognition : Multimodal semantic information fusion—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
Anti-Compression Contrastive Facial Forgery Detection—0
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction—0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation—0
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning—0
Approximate Bayesian Computation via Classification—0
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
A Probabilistic Interpretation of Transformers—0
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition—0
A Rate-Distortion Approach to Domain Generalization—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
ARIA: Adversarially Robust Image Attribution for Content Provenance—0
A Risk Communication Event Detection Model via Contrastive Learning—0
A Robust Contrastive Alignment Method For Multi-Domain Text Classification—0
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
A Scalable Holistic approach for Age and Gender inference of Twitter Users—0
A Self-Learning Multimodal Approach for Fake News Detection—0
A Self-supervised Contrastive Learning Method for Grasp Outcomes Prediction—0
A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification—0
A Self-supervised Mixed-curvature Graph Neural Network—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