MobileNetV2 is the second generation of MobileNet that introduces inverted residual blocks with linear bottlenecks — expanding channels before depthwise convolution (inverted from ResNet's bottleneck) and removing the activation function after the final projection.

What Is MobileNetV2?

• Inverted Residual: Expand (1×1) -> Depthwise (3×3) -> Project (1×1). Expansion ratio $t = 6$.
• Linear Bottleneck: No activation (ReLU) after the final 1×1 projection to prevent information loss in low-dimensional features.
• Skip Connection: Residual connection between the narrow bottleneck features (not the expanded features).
• Paper: Sandler et al. (2018).

Why It Matters

• SSD/Detection: The default mobile backbone for object detection and segmentation on device.
• Information Manifold: The linear bottleneck insight — ReLU in low dimensions destroys information — is theoretically motivated.
• Industry Standard: Used in TensorFlow Lite, MediaPipe, and countless mobile applications.

MobileNetV2 is the inverted bottleneck revolution — proving that expanding before filtering and projecting linearly produces better mobile features.