Can we identify and remove less important feature maps or individual activations within feature maps without significantly degrading performance?

Oct 12, 2021 ... In this paper, we propose an efficient hardware accelerator with an interlayer feature compression technique to significantly reduce the required on-chip ... Jul 13, 2021 ... ... feature map. However, in CNN, pooling is applied to feature maps that result from each filter. Flattening. You can think of CNN as a sequence ... In this paper, we propose strategies to enable CNN inference accelerators to have an adjustable feature map compression ratio, making them tunable regarding ... CNN Feature Maps and Object Saliency Maps. Wataru Shimoda and Keiji Yanai ... map can be obtained by up-sampling saliency maps of the intermediate ... Jun 27, 2021 ... Feature maps in CNN. Now, let's go over a few important terms that ... feature map containing the most dominant features of the previous feature ... Sep 17, 2018 ... These so-called feature maps are the outputs of the convolutions that take place using the input color channels and the convolutional filters. Feb 22, 2019 ... Specifically, the task involves identifying useful 2D-CNN input feature maps for enhancing speaker identification with an ultimate goal to ... Jun 29, 2021 ... This paper proposes an emotion recognition method based on three-dimensional (3D) feature maps and CNNs. Implementation details. The conventional CNN has three convolutional layers with 96, 128 and 256 filters, re- spectively. Each layer consists of a 5 × ... May 4, 2021 ... correlation coefficient between each multiunit feature map and every CNN feature map. ... feature maps and used a generative adversarial ...