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 6426–6450 of 6661 papers

TitleStatusHype
Semi-supervised Intent Discovery with Contrastive Learning—0
Semi-Supervised Learning for Mars Imagery Classification and Segmentation—0
Semi-supervised News Discourse Profiling with Contrastive Learning—0
Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty—0
Semi-Supervised Relational Contrastive Learning—0
Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving—0
SemST: Semantically Consistent Multi-Scale Image Translation via Structure-Texture Alignment—0
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation—0
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition—0
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval—0
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts—0
SenTest: Evaluating Robustness of Sentence Encoders—0
Separated Contrastive Learning for Matching in Cross-domain Recommendation with Curriculum Scheduling—0
Sequential Contrastive Audio-Visual Learning—0
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention—0
SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks—0
SetCSE: Set Operations using Contrastive Learning of Sentence Embeddings—0
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks—0
SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification—0
SGAligner: 3D Scene Alignment with Scene Graphs—0
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Shapley Value-based Contrastive Alignment for Multimodal Information Extraction—0
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data—0
Sharpness & Shift-Aware Self-Supervised Learning—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