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 1351–1375 of 6661 papers

TitleStatusHype
Leveraging Superfluous Information in Contrastive Representation Learning—0
Enforcing View-Consistency in Class-Agnostic 3D Segmentation Fields—0
Uniting contrastive and generative learning for event sequences models—0
3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Structure-enhanced Contrastive Learning for Graph Clustering—0
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
PLUTUS: A Well Pre-trained Large Unified Transformer can Unveil Financial Time Series Regularities—0
WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
3C: Confidence-Guided Clustering and Contrastive Learning for Unsupervised Person Re-IdentificationCode0
Enhancing Modal Fusion by Alignment and Label Matching for Multimodal Emotion RecognitionCode1
Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio PairsCode0
ConVerSum: A Contrastive Learning-based Approach for Data-Scarce Solution of Cross-Lingual Summarization Beyond Direct Equivalents—0
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing—0
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models—0
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition—0
ECG-Chat: A Large ECG-Language Model for Cardiac Disease DiagnosisCode2
ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language—0
PITN: Physics-Informed Temporal Networks for Cuffless Blood Pressure EstimationCode1
Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma GradingCode0
EasyRec: Simple yet Effective Language Models for RecommendationCode2
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning—0
The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I TranslationCode1
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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