MobileNetV2 is a revolutionary convolutional neural network architecture specifically designed for mobile and embedded devices. Unlike traditional neural networks that require significant computational resources, MobileNetV2 achieves an optimal balance between accuracy and efficiency. It features low computational cost, small memory footprint, while maintaining good accuracy for various computer vision tasks.
The key innovation in MobileNetV2 is the use of depthwise separable convolutions. Traditional convolutions are computationally expensive because they process all input channels simultaneously. MobileNetV2 breaks this into two efficient steps: first, depthwise convolution filters each input channel separately using three by three filters. Then, pointwise convolution combines the channels using one by one convolutions. This factorization dramatically reduces computational complexity while maintaining the network's representational power.
The inverted residual block is MobileNetV2's most innovative feature. Unlike traditional residual blocks that compress features through a narrow bottleneck, inverted residuals start narrow, expand to a wider representation for processing, then compress back to narrow output. This design preserves important information in low-dimensional spaces and uses linear bottlenecks without ReLU activation to prevent information loss. The residual connection links the narrow input and output, enabling better gradient flow and more efficient training.
Linear bottlenecks are a crucial innovation in MobileNetV2. Traditional neural networks use ReLU activation everywhere, but ReLU can be destructive in low-dimensional spaces by setting negative values to zero. When features are compressed to narrow bottlenecks, this information loss becomes significant. MobileNetV2 solves this by using linear activation in the narrow projection layers, preserving all feature information. ReLU is still used in high-dimensional expansion and depthwise layers where information loss is less critical.
To summarize what we have learned about MobileNetV2: This architecture revolutionizes mobile computer vision through three key innovations. Depthwise separable convolutions reduce computation by factorizing standard convolutions. Inverted residual blocks improve information flow by expanding then compressing features. Linear bottlenecks preserve important information in narrow layers. Together, these techniques enable high-quality computer vision applications on mobile and embedded devices with limited computational resources.