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 3051–3075 of 6661 papers

TitleStatusHype
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation—0
iEdit: Localised Text-guided Image Editing with Weak Supervision—0
Conversation Disentanglement with Bi-Level Contrastive Learning—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Contrastive Learning in Memristor-based Neuromorphic Systems—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
Image-free Domain Generalization via CLIP for 3D Hand Pose Estimation—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment—0
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
GCC: Generative Calibration Clustering—0
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients—0
A dual contrastive framework—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
StackMix: A complementary Mix algorithm—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
Contrastive Learning from Synthetic Audio Doppelgängers—0
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching—0
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models—0
CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition—0
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—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