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 3426–3450 of 6661 papers

TitleStatusHype
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
Using Multiple Instance Learning to Build Multimodal Representations—0
Using Navigational Information to Learn Visual Representations—0
Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge—0
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video—0
Using system context information to complement weakly labeled data—0
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression—0
Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging—0
USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation—0
UTC: A Unified Transformer with Inter-Task Contrastive Learning for Visual Dialog—0
Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model—0
Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition—0
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation—0
Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue—0
Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling—0
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models—0
VaCDA: Variational Contrastive Alignment-based Scalable Human Activity Recognition—0
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning—0
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image—0
Variance-Aware Loss Scheduling for Multimodal Alignment in Low-Data Settings—0
Variational Supervised 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