Methods › Computer Vision › Convolutional Neural Networks › AmoebaNet

AmoebaNet

7 papers tagged archive 2025-07-28

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

AmoebaNet is a convolutional neural network found through regularized evolution architecture search. The search space is NASNet, which specifies a space of image classifiers with a fixed outer structure: a feed-forward stack of Inception-like modules called cells. The discovered architecture is shown to the right.

Source: Regularized Evolution for Image Classifier Architecture SearchSee Code · tensorflow/tpu

Papers archive 2025-07-28

7 shown of 7, 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

19 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 Classification4
Neural Architecture Search4
Object Detection4
object-detection4
GPU2
Keypoint Detection2
Object2
Semantic Segmentation2
image-classification2
Data Augmentation1
Evolutionary Algorithms1
Fine-Grained Image Classification1
Graph Neural Network1
Image Augmentation1
Machine Translation1
Real-Time Object Detection1
Reinforcement Learning1
Robust Object Detection1
Translation1

Usage over time archive 2025-07-28

Papers per year tagged with AmoebaNet: 2018 to 2021, peak 3 3 0 2018: 2 papers 2018 2019: 3 papers 2019 2020: 1 paper 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (7 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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