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

Showing 54765500 of 6661 papers

TitleStatusHype
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence EmbeddingsCode1
Dual Cluster Contrastive learning for Object Re-IdentificationCode0
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual RepresentationsCode1
Contextualized Spatio-Temporal Contrastive Learning with Self-SupervisionCode0
Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient FrameworkCode1
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension0
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph EmbeddingsCode1
Boosting Contrastive Learning with Relation Knowledge Distillation0
Contrastive Instruction-Trajectory Learning for Vision-Language NavigationCode0
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization0
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework0
Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy DetectionCode1
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation LearningCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Suppressing Static Visual Cues via Normalizing Flows for Self-Supervised Video Representation LearningCode0
Time-Equivariant Contrastive Video Representation Learning0
Seeing Objects in dark with Continual Contrastive Learning0
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited AnnotationCode1
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding0
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive LearningCode1
Joint Learning of Localized Representations from Medical Images and ReportsCode1
VarCLR: Variable Semantic Representation Pre-training via Contrastive LearningCode1
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks0
U2-Former: A Nested U-shaped Transformer for Image Restoration0
Transferring Unconditional to Conditional GANs with Hyper-ModulationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified