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 201–225 of 6661 papers

TitleStatusHype
A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware CapabilityCode0
Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G NetworksCode1
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback—0
Negative Metric Learning for Graphs—0
Robust Federated Learning on Edge Devices with Domain Heterogeneity—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records—0
MAKE: Multi-Aspect Knowledge-Enhanced Vision-Language Pretraining for Zero-shot Dermatological AssessmentCode1
Improving Unsupervised Task-driven Models of Ventral Visual Stream via Relative Position PredictivityCode0
DFA-CON: A Contrastive Learning Approach for Detecting Copyright Infringement in DeepFake Art—0
Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware RecommendationCode1
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge AcquisitionCode0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection—0
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Multimodal Fake News Detection: MFND Dataset and Shallow-Deep Multitask LearningCode1
A Vision-Language Foundation Model for Leaf Disease IdentificationCode0
MarkMatch: Same-Hand Stuffing Detection—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling LawsCode0
Weakly Supervised Temporal Sentence Grounding via Positive Sample Mining—0
Towards Robust Few-Shot Text Classification Using Transformer Architectures and Dual Loss Strategies—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