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 2501–2550 of 6661 papers

TitleStatusHype
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning—0
Center Contrastive Loss for Metric Learning—0
Hierarchical Self-supervised Representation Learning for Movie Understanding—0
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization—0
CellCLIP -- Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning—0
A comprehensive solution to retrieval-based chatbot construction—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets—0
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations—0
CEIA: CLIP-Based Event-Image Alignment for Open-World Event-Based Understanding—0
Just Functioning as a Hook for Two-Stage Referring Multi-Object Tracking—0
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—0
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
Counting Objects in a Robotic Hand—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
A Robust Contrastive Alignment Method For Multi-Domain Text Classification—0
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery—0
A Risk Communication Event Detection Model via Contrastive Learning—0
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding—0
Counter-Contrastive Learning for Language GANs—0
CDA: Contrastive-adversarial Domain Adaptation—0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation—0
CoTSRF: Utilize Chain of Thought as Stealthy and Robust Fingerprint of Large Language Models—0
CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation—0
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval—0
CCML: Curriculum and Contrastive Learning Enhanced Meta-Learner for Personalized Spatial Trajectory Prediction—0
ARIA: Adversarially Robust Image Attribution for Content Provenance—0
COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport—0
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation—0
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future—0
Hierarchical Cross Contrastive Learning of Visual Representations—0
CoSP: Co-supervised pretraining of pocket and ligand—0
CCFC++: Enhancing Federated Clustering through Feature Decorrelation—0
CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imaging—0
Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network—0
Correlation-aware active learning for surgery video segmentation—0
Causality-based Dual-Contrastive Learning Framework for Domain Generalization—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks—0
CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts—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