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 4251–4275 of 6661 papers

TitleStatusHype
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence EmbeddingsCode1
Clinical Contrastive Learning for Biomarker Detection—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning—0
Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning—0
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data—0
On Negative Sampling for Contrastive Audio-Text Retrieval—0
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive LearningCode0
GOOD-D: On Unsupervised Graph Out-Of-Distribution DetectionCode1
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI—0
Black-Box Attack against GAN-Generated Image Detector with Contrastive PerturbationCode1
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness PredictionsCode0
Graph Contrastive Learning with Implicit AugmentationsCode0
Camera Alignment and Weighted Contrastive Learning for Domain Adaptation in Video Person ReIDCode0
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations—0
Contrastive Classification and Representation Learning with Probabilistic Interpretation—0
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence EmbeddingCode0
Contrastive Learning enhanced Author-Style Headline GenerationCode0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio EffectsCode1
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
MarginNCE: Robust Sound Localization with a Negative Margin—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