Parameter-Free Channel Attention for Image Classification and Super-Resolution

Understand

The channel attention mechanism is a useful technique widely employed in deep convolutional neural networks to boost the performance for image processing tasks, eg, image classification and image super-resolution.

• It is usually designed as a parameterized sub-network and embedded into the convolutional layers of the network to learn more powerful feature representations.
• However, current channel attention induces more parameters and therefore leads to higher computational costs.
• To deal with this issue, in this work, we propose a Parameter-Free Channel Attention (PFCA) module to boost the performance of popular image classification and image super-resolution networks, but completely sweep out the parameter growth of channel attention.