Search Results for author: Ahmed Osman

Found 7 papers, 4 papers with code

Training Generative Adversarial Networks for Optical Property Mapping using Synthetic Image Data

no code implementations15 Mar 2022 Ahmed Osman, Jane Crowley, George Gordon

In future we expect that application of techniques such as domain adaptation or training on hybrid real-synthetic datasets will create a powerful tool for fast, accurate production of optical property maps from real clinical imaging systems.

Domain Adaptation Generative Adversarial Network

Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI

2 code implementations16 Mar 2020 Leila Arras, Ahmed Osman, Wojciech Samek

The rise of deep learning in today's applications entailed an increasing need in explaining the model's decisions beyond prediction performances in order to foster trust and accountability.

Benchmarking Explainable Artificial Intelligence (XAI) +4

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

1 code implementation27 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.

Neural Network Compression Quantization +1

Evaluating Recurrent Neural Network Explanations

1 code implementation WS 2019 Leila Arras, Ahmed Osman, Klaus-Robert Müller, Wojciech Samek

Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs.

Negation Sentence +1

Dual Recurrent Attention Units for Visual Question Answering

1 code implementation1 Feb 2018 Ahmed Osman, Wojciech Samek

First, we introduce a baseline VQA model with visual attention and test the performance difference between convolutional and recurrent attention on the VQA 2. 0 dataset.

Question Answering Visual Question Answering

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