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 2526–2550 of 6661 papers

TitleStatusHype
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