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](https://paperswithcode.com/method/mobilenetv2) CNN architecture. It has since been reused for several mobile-optimized CNNs.

A traditional [Residual Block](https://paperswithcode.com/method/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](https://paperswithcode.com/method/1x1-convolution). The number of channels is then increased again with a 1x1 [convolution](https://paperswithcode.com/method/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](https://paperswithcode.com/method/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.

**OA-Mix** is a general and effective data augmentation method for single-domain generalization in object detection. It increases image diversity while preserving important semantic features with
multi-level transformations and object-aware mixing.

OA-Mix

Object-Aware Domain Generalization for Object Detection

Inverted Residual Block

MobileNetV2: Inverted Residuals and Linear Bottlenecks

Pathology Language and Image Pre-Training (PLIP) is a vision-and-language foundation model created by fine-tuning CLIP on pathology images.

Source	MobileNetV2: Inverted Residuals and Linear Bottlenecks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com