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 3726–3750 of 6661 papers

TitleStatusHype
Anatomical Structure-Guided Medical Vision-Language Pre-training—0
Anatomy-Aware Conditional Image-Text Retrieval—0
An Attention-based Framework for Fair Contrastive Learning—0
An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning—0
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning—0
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022—0
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis—0
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
An Image-based Approach of Task-driven Driving Scene Categorization—0
Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity—0
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer—0
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise—0
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging—0
An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images—0
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification—0
An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding—0
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment—0
Annotation-Efficient Untrimmed Video Action Recognition—0
Anomalies, Representations, and Self-Supervision—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Anomaly Detection via Multi-Scale Contrasted Memory—0
An online algorithm for contrastive Principal Component Analysis—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