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Inverted Residual Block

823 papers tagged archive 2025-07-28

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

An Inverted Residual Block, sometimes called an MBConv Block, is a type of residual block used for image models that uses an inverted structure for efficiency reasons. It was originally proposed for the MobileNetV2 CNN architecture. It has since been reused for several mobile-optimized CNNs.

A traditional Residual Block has a wide -> narrow -> wide structure with the number of channels. The input has a high number of channels, which are compressed with a 1x1 convolution. The number of channels is then increased again with a 1x1 convolution so input and output can be added.

In contrast, an Inverted Residual Block follows a narrow -> wide -> narrow approach, hence the inversion. We first widen with a 1x1 convolution, then use a 3x3 depthwise convolution (which greatly reduces the number of parameters), then we use a 1x1 convolution to reduce the number of channels so input and output can be added.

Source: MobileNetV2: Inverted Residuals and Linear BottlenecksSee Code · pytorch/vision

Papers archive 2025-07-28

30 shown of 823, 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 414 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 Classification161
Transfer Learning126
image-classification117
Object Detection86
Neural Architecture Search74
object-detection74
Classification61
Quantization59
Semantic Segmentation56
Deep Learning47
Data Augmentation42
Diagnostic38
General Classification37
GPU35
Segmentation28
Decoder20
Instance Segmentation19
Knowledge Distillation19
Model Compression19
Computational Efficiency18

Usage over time archive 2025-07-28

Papers per year tagged with Inverted Residual Block: 2018 to 2025, peak 160 160 0 2018: 14 papers 2018 2019: 73 papers 2019 2020: 118 papers 2020 2021: 160 papers 2021 2022: 129 papers 2022 2023: 139 papers 2023 2024: 136 papers 2024 2025: 54 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (823 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 Blocks

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