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 6176–6200 of 6661 papers

TitleStatusHype
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation—0
RepsNet: Combining Vision with Language for Automated Medical Reports—0
An Effective Deployment of Contrastive Learning in Multi-label Text Classification—0
Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition—0
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning—0
REST: REtrieve & Self-Train for generative action recognition—0
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning—0
Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning—0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
Rethinking Audio-visual Synchronization for Active Speaker Detection—0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
Rethinking Deep Contrastive Learning with Embedding Memory—0
Rethinking Graph Contrastive Learning through Relative Similarity Preservation—0
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach—0
Rethinking Positive Pairs in Contrastive Learning—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
Rethinking Samples Selection for Contrastive Learning: Mining of Potential Samples—0
Rethinking Self-Supervision Objectives for Generalizable Coherence Modeling—0
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion—0
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor 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