CHASE: Channel-Wise and Spatial Attention for Early Exiting in Image Classification

Dynamic early-exiting neural networks have been proposed for image classification to balance the trade-off between classification performance and inference cost. In this context, we propose a multi-exit neural network architecture that exploits the power of attention mechanisms, which improve performance but incur significant computational overhead. In CHASE, we introduce two attention-like mechanisms to go beyond existing multi-exit architectures. The first mechanism dynamically adjusts the importance of different feature channels and spatial locations, recalibrating channel-wise feature responses. The second mechanism, based on self-attention, aggregates features from different spatial locations at the end of the network. We evaluate the proposed architecture on the CIFAR and ImageNet datasets, comparing it with the original network and other state-of-the-art approaches. Our results show that the proposed architecture achieves competitive performance in terms of accuracy and computational efficiency.