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 3626–3650 of 6661 papers

TitleStatusHype
Open-Vocabulary Object Detection with Meta Prompt Representation and Instance Contrastive Optimization—0
Unsupervised Modality-Transferable Video Highlight Detection with Representation Activation Sequence Learning—0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Anatomical Structure-Guided Medical Vision-Language Pre-training—0
CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow—0
Deep Submodular Peripteral Networks—0
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?—0
MolBind: Multimodal Alignment of Language, Molecules, and Proteins—0
Intra-video Positive Pairs in Self-Supervised Learning for UltrasoundCode0
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model—0
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing ModelsCode0
Calibrating Multi-modal Representations: A Pursuit of Group Robustness without AnnotationsCode0
Spatiotemporal Representation Learning for Short and Long Medical Image Time SeriesCode0
Self-supervised Contrastive Learning for Implicit Collaborative Filtering—0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation—0
Disentangling Policy from Offline Task Representation Learning via Adversarial Data AugmentationCode0
Learn and Search: An Elegant Technique for Object Lookup using Contrastive Learning—0
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)—0
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature AlignmentCode0
Exploiting Style Latent Flows for Generalizing Deepfake Video Detection—0
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Understanding and Mitigating Human-Labelling Errors in Supervised Contrastive Learning—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