Convolution is one of the core building blocks of deep learning models. It involves applying a filter over an input image to extract features. In standard convolution, the filter performs both channelwise and spatial-wise computation in a single step. However, a new approach called Depthwise Separable Convolution has recently emerged that splits the computation into two steps, offering several advantages over traditional convolution.
What is Depthwise Separable Convolution?
Depthwise Separable Convolution consists of two main components: depthwise convolution and pointwise convolution. In depthwise convolution, a single convolutional filter is applied to each input channel. The output of the depthwise convolution is then fed into pointwise convolution, which creates a linear combination of the output while preserving channelwise information.
Let's break this down further. Traditional convolution applies a filter to each input channel separately, resulting in multiple outputs. It then combines these outputs by adding them together. On the other hand, depthwise convolution applies a single filter to each input channel separately, resulting in a set of channelwise outputs. This means that each output maintains the spatial information of the original input.
Pointwise convolution is used to combine the outputs of depthwise convolution. It applies a 1x1 filter to each channel, enabling the creation of linear combinations between channels. The result is a feature map that preserves both the spatial and channelwise information of the input.
Advantages of Depthwise Separable Convolution
Depthwise Separable Convolution offers several advantages over traditional convolution.
Efficiency and Speed
Depthwise Separable Convolution requires fewer parameters than traditional convolution. This means that it can achieve similar accuracy with a smaller number of parameters, making models more efficient and faster. Additionally, because depthwise convolution operates on each channel individually, it can be run on parallel on different processors, further speeding up the computation.
Reduced Overfitting
Traditional convolution can create highly complex models that are prone to overfitting. In contrast, the depthwise separable convolution forces the model to learn more general features. By splitting the computation into two steps, the model can focus on learning spatial information in one step and channelwise information in the other.
Better Use of Resources
The reduced computation of depthwise separable convolution makes it possible to run more experiments in less time. This means that a larger number of models can be trained on the same resources, leading to better results. Additionally, because depthwise convolution operates on each channel individually, it can be used to evaluate models with high-resolution images.
Applications of Depthwise Separable Convolution
Depthwise Separable Convolution has been used in various deep learning applications, including computer vision and natural language processing. One example is MobileNet, a popular CNN architecture that uses depthwise separable convolution to provide high accuracy with a small number of parameters. Another example is the Google Translate neural machine translation model, which uses depthwise separable convolution to reduce computation time and improve accuracy.
Depthwise Separable Convolution is a powerful technique that can improve the efficiency and speed of deep learning models. By splitting the computation into two steps, it allows models to learn more generalized features, reducing overfitting and better utilizing resources. It has already shown promise in applications such as computer vision and natural language processing and is likely to become even more prevalent in the near future.
