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 1426–1450 of 6661 papers

TitleStatusHype
A Debiased Nearest Neighbors Framework for Multi-Label Text Classification—0
Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced RecommendationCode0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
ConDL: Detector-Free Dense Image Matching—0
Text Conditioned Symbolic Drumbeat Generation using Latent Diffusion ModelsCode0
Feedback Reciprocal Graph Collaborative Filtering—0
StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement—0
Contrastive Learning and Abstract Concepts: The Case of Natural Numbers—0
A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction—0
Contrastive Learning-based Chaining-Cluster for Multilingual Voice-Face AssociationCode0
Symmetric Graph Contrastive Learning against Noisy Views for RecommendationCode0
MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph—0
Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples—0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Regularized Contrastive Partial Multi-view Outlier Detection—0
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations—0
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps—0
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment—0
DD-rPPGNet: De-interfering and Descriptive Feature Learning for Unsupervised rPPG Estimation—0
Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning—0
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks—0
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-TuningCode1
Harvesting Textual and Structured Data from the HAL Publication Repository—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