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 2251–2300 of 6661 papers

TitleStatusHype
Deep Clustering by Semantic Contrastive Learning—0
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks—0
CLERF: Contrastive LEaRning for Full Range Head Pose Estimation—0
Deep Bregman Divergence for Contrastive Learning of Visual Representations—0
A Supervised Contrastive Learning Pretrain-Finetune Approach for Time Series—0
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
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
Generative Modeling of Class Probability for Multi-Modal 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
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
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
Decentralized Collective World Model for Emergent Communication and Coordination—0
Debiasing Multimodal Sarcasm Detection with Contrastive Learning—0
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector—0
Debiased Novel Category Discovering and Localization—0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
Debiased Model-based Interactive Recommendation—0
CLCE: An Approach to Refining Cross-Entropy and Contrastive Learning for Optimized Learning Fusion—0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Contrastive Representation Disentanglement for Clustering—0
Debiased Contrastive Learning of Unsupervised Sentence Representations—0
(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)—0
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—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