114 citations · 165 across the 4 of their papers we have counts for
4 papers
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Christopher J. Anders, Leander Weber, David Neumann +3
Contemporary learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples. These may, however, contain biases, artifacts, o…
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage +10
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for lit…
DeepCABAC: Context-adaptive binary arithmetic coding for deep neural network compression
Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage +9
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks. It quantizes each weight parameter by minimizing a weighted rate-distor…
Multi-Kernel Prediction Networks for Denoising of Burst Images
Talmaj Marinč, Vignesh Srinivasan, Serhan Gül +2
In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and…