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 2251–2275 of 6661 papers

TitleStatusHype
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals—0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
Generalization Bounds for Adversarial Contrastive Learning—0
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders—0
Decoupled Spatial Temporal Graphs for Generic Visual Grounding—0
ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery—0
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection—0
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting—0
CLEAR: Contrastive Learning for Sentence Representation—0
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment—0
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization—0
Generalization Analysis for Contrastive Representation Learning—0
Deconstruct Complexity (DeComplex): A Novel Perspective on Tackling Dense Action Detection—0
CLDTA: Contrastive Learning based on Diagonal Transformer Autoencoder for Cross-Dataset EEG Emotion Recognition—0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—0
Age Prediction From Face Images Via Contrastive Learning—0
Decoding Realistic Images from Brain Activity with Contrastive Self-supervision and Latent Diffusion—0
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering—0
Generalization Analysis for Deep Contrastive Representation Learning—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—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