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 3851–3875 of 6661 papers

TitleStatusHype
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
Adversarial Masking Contrastive Learning for vein recognition—0
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition—0
SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation—0
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization—0
Contrastive Learning with Negative Sampling Correction—0
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language ModelsCode0
CCFC: Bridging Federated Clustering and Contrastive LearningCode0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing—0
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration—0
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image—0
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp DetectionCode0
BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation—0
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive LearningCode0
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking—0
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction—0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
Connect Later: Improving Fine-tuning for Robustness with Targeted AugmentationsCode0
Two-stream joint matching method based on contrastive learning for few-shot action recognition—0
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification—0
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent PredictionCode0
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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