Methods › General › Skip Connection Blocks › FBNet Block

FBNet Block

12 papers tagged archive 2025-07-28

Introduced by Bichen Wu et al. in FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search

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

FBNet Block is an image model block used in the FBNet architectures discovered through DNAS neural architecture search. The basic building blocks employed are depthwise convolutions and a residual connection.

PaperSourceSee Code · AnnaAraslanova/FBNet

Papers archive 2025-07-28

12 shown of 12, 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 22 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
Neural Architecture Search7
Hardware Aware Neural Architecture Search4
Image Classification3
Script Generation2
Segmentation2
Semantic Segmentation2
image-classification2
Autonomous Driving1
Benchmarking1
Depth Estimation1
GPU1
Image Segmentation1
Instance Segmentation1
Meta-Learning1
Multi-Task Learning1
Object1
Object Detection1
Point Cloud Completion1
Quantization1
Scene Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with FBNet Block: 2018 to 2023, peak 5 5 0 2018: 1 paper 2018 2019: 1 paper 2019 2020: 2 papers 2020 2021: 5 papers 2021 2022: 2 papers 2022 2023: 1 paper 2023
Papers per year the archive tags with this method, by the paper's archive date (12 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

Skip Connection BlocksImage Model Blocks

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