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 2326–2350 of 6661 papers

TitleStatusHype
Classification and Clustering of Sentence-Level Embeddings of Scientific Articles Generated by Contrastive Learning—0
A Generic Method for Fine-grained Category Discovery in Natural Language Texts—0
Goal-conditioned reinforcement learning for ultrasound navigation guidance—0
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution—0
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
Data curation via joint example selection further accelerates multimodal learning—0
Class-aware and Augmentation-free Contrastive Learning from Label Proportion—0
3D Scene Graph Guided Vision-Language Pre-training—0
GMM-Based Comprehensive Feature Extraction and Relative Distance Preservation For Few-Shot Cross-Modal Retrieval—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models—0
Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification—0
DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash—0
A General Purpose Supervisory Signal for Embodied Agents—0
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation—0
DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation—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