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 2051–2075 of 6661 papers

TitleStatusHype
Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables—0
LLM-CoT Enhanced Graph Neural Recommendation with Harmonized Group Policy Optimization—0
Contrastive Alignment with Semantic Gap-Aware Corrections in Text-Video RetrievalCode0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding—0
Not All Documents Are What You Need for Extracting Instruction Tuning Data—0
Robust Cross-View Geo-Localization via Content-Viewpoint Disentanglement—0
Towards Sustainability in 6G Network Slicing with Energy-Saving and Optimization Methods—0
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement—0
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation—0
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning—0
Fractal Graph Contrastive Learning—0
A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware CapabilityCode0
FRET: Feature Redundancy Elimination for Test Time Adaptation—0
Robust Federated Learning on Edge Devices with Domain Heterogeneity—0
Negative Metric Learning for Graphs—0
Less is More: Multimodal Region Representation via Pairwise Inter-view LearningCode0
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback—0
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning—0
Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records—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
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
EAGLE: Contrastive Learning for Efficient Graph Anomaly Detection—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