Methods › Computer Vision › Image Models › WideResNet

WideResNet

58 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Wide Residual Networks are a variant on ResNets where we decrease depth and increase the width of residual networks. This is achieved through the use of wide residual blocks.

Source: Wide Residual NetworksSee Code · osmr/imgclsmob

Papers archive 2025-07-28

30 shown of 58, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 60 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Classification20
image-classification10
Out-of-Distribution Detection7
Object Detection6
Adversarial Robustness5
Anomaly Detection5
General Classification5
object-detection5
Data Augmentation4
Deep Learning3
Model Compression3
Anomaly Localization2
Classification2
Contrastive Learning2
Diversity2
Knowledge Distillation2
Network Pruning2
Neural Architecture Search2
Out of Distribution (OOD) Detection2
Representation Learning2

Usage over time archive 2025-07-28

Papers per year tagged with WideResNet: 2016 to 2024, peak 16 16 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 6 papers 2018 2019: 11 papers 2019 2020: 16 papers 2020 2021: 8 papers 2021 2022: 9 papers 2022 2023: 4 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (58 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Models

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