Methods › Computer Vision › Convolutional Neural Networks › Inception-ResNet-v2

Inception-ResNet-v2

18 papers tagged archive 2025-07-28

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

Inception-ResNet-v2 is a convolutional neural architecture that builds on the Inception family of architectures but incorporates residual connections (replacing the filter concatenation stage of the Inception architecture).

Source: Inception-v4, Inception-ResNet and the Impact of...See Code · Cadene/pretrained-models.pytorch

Papers archive 2025-07-28

18 shown of 18, 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 42 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
Transfer Learning5
Decoder3
General Classification3
Image Classification3
Classification2
Machine Translation2
3D Reconstruction1
Action Recognition1
Activity Recognition1
All1
Breast Cancer Detection1
Caption Generation1
Classifier calibration1
Colorization1
Contrastive Learning1
Data Augmentation1
Depth Estimation1
Depth Prediction1
Diversity1
Fine-Grained Image Classification1

Usage over time archive 2025-07-28

Papers per year tagged with Inception-ResNet-v2: 2016 to 2025, peak 4 4 0 2016: 2 papers 2016 2017: 1 paper 2017 2018: 1 paper 2018 2019: 4 papers 2019 2020: 3 papers 2020 2021: 1 paper 2021 2022: 2 papers 2022 2023: 1 paper 2023 2024: 2 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (18 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

Convolutional Neural Networks

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