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 1701–1725 of 6661 papers

TitleStatusHype
Contrastive Learning from Synthetic Audio Doppelgängers—0
A DeNoising FPN With Transformer R-CNN for Tiny Object DetectionCode2
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning—0
Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 LossCode0
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training ModelsCode1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal LearningCode1
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language AlignmentCode1
QAGCF: Graph Collaborative Filtering for Q&A Recommendation—0
Skill-aware Mutual Information Optimisation for Generalisation in Reinforcement LearningCode1
MA-AVT: Modality Alignment for Parameter-Efficient Audio-Visual TransformersCode0
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement—0
Confidence-aware Contrastive Learning for Selective ClassificationCode0
Denoising-Aware Contrastive Learning for Noisy Time SeriesCode1
Road Network Representation Learning with the Third Law of Geography—0
JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model EditsCode0
Low-Rank Similarity Mining for Multimodal Dataset DistillationCode1
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive LearningCode1
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing—0
ConPCO: Preserving Phoneme Characteristics for Automatic Pronunciation Assessment Leveraging Contrastive Ordinal Regularization—0
RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding using Contrastive Learning with Application to Room Shape Classification—0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image SegmentationCode1
MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item DetectionCode0
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection—0
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and BeyondCode0
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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