Squeeze-and-Excitation Block: Boosting Network Representational Power
As technology advances, machines are becoming increasingly adept at learning from data with deep neural networks. However, even the most advanced models can fall short in representing complex features in the data. The Squeeze-and-Excitation Block (SE Block) was designed to address this issue by enabling networks to perform dynamic channel-wise feature recalibration.
At its core, the SE Block is an architectural unit that is inserted into a neural network. It processes the output of a convolutional block using several steps to refine the learned features.
The Process
The SE Block operates in a series of 4 steps:
- Squeeze: The input to the SE Block is a convolutional block with multiple channels. Each channel is "squeezed" by average pooling into a single value. This process reduces the dimensions of the channels by computing the mean of the feature values within each channel.
- Excitation: The pooled values are then fed into a series of dense layers to learn the inter-dependencies between the different channels. The first dense layer serves to add non-linearity while the second dense layer uses a sigmoid activation function to generate channel-wise attention scores. These scores effectively weigh each channel's importance in capturing the features of the data.
- Scaling: The excitation process generates a set of attention scores for each channel. To produce meaningful influence on the input feature maps, the scores are rescaled by vector multiplication of the original feature maps with the excitation scores.
- Final Output: The output from this process is then fed to the next layer in the network where the activations are further processed.
Improving Network Performance
Why is such a process important? Using an SE Block enables the neural network to recalibrate the feature representation of the channels on a per-sample basis. This recalibration adjusts the learned features based on the input data, improving the network's representation power. In particular, the SE Block can effectively emphasize the most important channels or features relevant for each specific input sample. At the same time, it reduces attention to less significant features, facilitating cross-channel dependencies, and enhancing the neural network’s generalization performance.
The SE Block’s implementation is straightforward and can be added to an existing neural network architecture with relatively few modifications. It has shown significant improvements in accuracy across a wide range of tasks, with negligible computational cost.
The SE Block is a simple but powerful architecture that provides a smart way to recalibrate the features of a neural network. It facilitates the automatic learning of feature importance for each channel and produces a slim network with overall improved performance. Incorporating an SE Block into a neural network can be a game-changer, giving researchers and practitioners a crucial tool to optimize their models' representational power.
