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 4901–4925 of 6661 papers

TitleStatusHype
Audio-Visual Contrastive Learning with Temporal Self-Supervision—0
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval—0
Symbolic Discovery of Optimization AlgorithmsCode0
Imitation from Observation With Bootstrapped Contrastive Learning—0
Anti-Compression Contrastive Facial Forgery Detection—0
Understanding Multimodal Contrastive Learning and Incorporating Unpaired DataCode0
ContrasInver: Ultra-Sparse Label Semi-supervised Regression for Multi-dimensional Seismic Inversion—0
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading—0
Contrastive Learning and the Emergence of Attributes Associations—0
Self-supervised pseudo-colorizing of masked cellsCode0
Fairness-aware Multi-view ClusteringCode0
Multispectral Contrastive Learning with Viewmaker NetworksCode0
HateProof: Are Hateful Meme Detection Systems really Robust?—0
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System PredictionCode0
Analyzing Multimodal Objectives Through the Lens of Generative Diffusion Guidance—0
End-to-end Semantic Object Detection with Cross-Modal Alignment—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Detecting Contextomized Quotes in News Headlines by Contrastive LearningCode0
Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person SearchCode0
Multi-view Feature Extraction based on Dual Contrastive Head—0
SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation—0
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset SelectionCode0
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection—0
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning—0
Spectral Augmentations for Graph Contrastive 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