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 1876–1900 of 6661 papers

TitleStatusHype
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
CluCDD:Contrastive Dialogue Disentanglement via ClusteringCode1
Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action LocalizationCode1
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic SegmentationCode1
On Equivariant and Invariant Learning of Object Landmark RepresentationsCode1
Unsupervised Episode Generation for Graph Meta-learningCode1
Unsupervised Feature Learning by Cross-Level Instance-Group DiscriminationCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
Directed Graph Contrastive LearningCode1
Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image RetrievalCode1
Combined Scaling for Zero-shot Transfer Learning—0
Combating the Bucket Effect:Multi-Knowledge Alignment for Medication Recommendation—0
Unified Framework for Feature Extraction based on Contrastive Learning—0
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix—0
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning—0
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
Colorectal Polyp Classification from White-light Colonoscopy Images via Domain Alignment—0
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training—0
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation—0
Collaborative Visual Place Recognition through Federated 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