Communication constraints are one of the major challenges preventing the wide-spread adoption of Federated Learning systems.
To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e. g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e. g., MAC operations are reduced to a few accumulations plus two multiplications).
1 code implementation • 27 Jul 2019 • Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek
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 little loss of information.
no code implementations • 15 May 2019 • Simon Wiedemann, Heiner Kirchhoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek
We present DeepCABAC, a novel context-adaptive binary arithmetic coder for compressing deep neural networks.
We propose a general framework for neural network compression that is motivated by the Minimum Description Length (MDL) principle.
Adversarial attacks on deep learning models have compromised their performance considerably.
The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model.