Hasso Plattner Institut
Imprint   Data Privacy

Ralf Krestel

You are here:   Home > Publications > Conference Papers > KI 20

KI 20

Dynamic Channel and Layer Gating in Convolutional Neural Networks


Convolutional neural networks (CNN) are getting more and more complex, needing enormous computing resources and energy. In this paper, we propose methods for conditional computation in the context of image classification that allows a CNN to dynamically use its channels and layers conditioned on the input. To this end, we combine light-weight gating modules that can make binary decisions without causing much computational overhead. We argue, that combining the recently proposed channel gating mechanism with layer gating can significantly reduce the computational cost of large CNNs. Using discrete optimization algorithms, the gating modules are made aware of the context in which they are used and decide whether a particular channel and/or a particular layer will be executed. This results in neural networks that adapt their own topology conditioned on the input image. Experiments using the CIFAR10 and MNIST datasets show how competitive results in image classification with respect to accuracy can be achieved while saving up to 50% computational resources.

Full Paper


Conference Homepage

KI 2020

Bibtex Entry


Watch our new MOOC in German about hate and fake in the Internet ("Trolle, Hass und Fake-News: Wie können wir das Internet retten?") on openHPI (link).

New Photos

I added some photos from my trip to Hildesheim.

Powered by CMSimple| Template: ge-webdesign.de| Login