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

Showing 44014425 of 6661 papers

TitleStatusHype
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning0
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval0
Improving Dense Contrastive Learning with Dense Negative Pairs0
Learning to Locate Visual Answer in Video Corpus Using QuestionCode0
Pre-Training Representations of Binary Code Using Contrastive Learning0
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural NetworksCode1
Contrastive Trajectory Similarity Learning with Dual-Feature AttentionCode1
Improving Long-tailed Object Detection with Image-Level Supervision by Multi-Task Collaborative LearningCode0
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search0
Robust Diversified Graph Contrastive Network for Incomplete Multi-view ClusteringCode0
Multilingual Representation Distillation with Contrastive Learning0
Improving Continual Relation Extraction through Prototypical Contrastive Learning0
Using Whole Slide Image Representations from Self-Supervised Contrastive Learning for Melanoma Concordance Regression0
Contrastive Bayesian Analysis for Deep Metric LearningCode1
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link PredictionCode1
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER0
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive LearningCode1
CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification0
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model PretrainingCode1
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive LearningCode0
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
Hierarchical Few-Shot Object Detection: Problem, Benchmark and MethodCode1
Revisiting Self-Supervised Contrastive Learning for Facial Expression RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified