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 6351–6400 of 6661 papers

TitleStatusHype
Self-supervised Graph Learning for Occasional Group Recommendation—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Self-Supervised Graph Transformer for Deepfake Detection—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation—0
Self-Supervised Image Representation Learning with Geometric Set Consistency—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss—0
Self-supervised Learning for Acoustic Few-Shot Classification—0
Self-supervised Learning for Anomaly Detection in Computational Workflows—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Self-supervised Learning for Semi-supervised Temporal Language Grounding—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
Self-supervised Learning from 100 Million Medical Images—0
Self-Supervised Learning of Gait-Based Biomarkers—0
Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization—0
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos—0
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis—0
Self-Supervised Moving Vehicle Detection from Audio-Visual Cues—0
Self-Supervised Multisensor Change Detection—0
Self-supervised Multi-view Clustering in Computer Vision: A Survey—0
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA—0
Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene—0
Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding—0
Self-Supervised Ranking for Representation Learning—0
Self-Supervised Relationship Probing—0
Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI—0
Self-supervised Representation Learning for Trip Recommendation—0
Self-Supervised Representation Learning from Arbitrary Scenarios—0
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax—0
Self-supervised representation learning via adaptive hard-positive mining—0
Self-Supervised Representation Learning via Latent Graph Prediction—0
Self-Supervised Representation Learning With MUlti-Segmental Informational Coding (MUSIC)—0
Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features—0
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking—0
SAIL: Self-Augmented Graph Contrastive Learning—0
Self-supervised Speaker Recognition Training Using Human-Machine Dialogues—0
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization—0
Self-supervised Temporal Learning—0
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation—0
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs—0
Self-supervised Transformer for Deepfake Detection—0
Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection—0
Self-supervised Video-centralised Transformer for Video Face Clustering—0
Self-Supervised Video GANs: Learning for Appearance Consistency and Motion Coherency—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
Self-Supervised Video Representation Learning with Motion-Contrastive Perception—0
Self-Supervised Video Representation Learning via Latent Time Navigation—0
Self-Supervised Video Representation Learning in a Heuristic Decoupled Perspective—0
Self-Supervised Video Representation Learning with Meta-Contrastive Network—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