Ahmed Osman

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. Recently, the field of explainable AI (XAI) has developed methods that provide such explanations for already trained neural networks. In computer vision tasks such explanations, termed heatmaps, visualize the contributions of individual pixels to the prediction. So far XAI methods along with their heatmaps were mainly validated qualitatively via human-based assessment, or evaluated through auxiliary proxy tasks such as pixel perturbation, weak object localization or randomization tests. Due to the lack of an objective and commonly accepted quality measure for heatmaps, it was debatable which XAI method performs best and whether explanations can be trusted at all. In the present work, we tackle the problem by proposing a ground truth based evaluation framework for XAI methods based on the CLEVR visual question answering task. Our framework provides a (1) selective, (2) controlled and (3) realistic testbed for the evaluation of neural network explanations. We compare ten different explanation methods, resulting in new insights about the quality and properties of XAI methods, sometimes contradicting with conclusions from previous comparative studies. The CLEVR-XAI dataset and the benchmarking code can be found at https://github.com/ahmedmagdiosman/clevr-xai.

Leila Arras, Ahmed Osman, Klaus-Robert Müller et al.

Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.

Ahmed Osman, Wojciech Samek

Visual Question Answering (VQA) requires AI models to comprehend data in two domains, vision and text. Current state-of-the-art models use learned attention mechanisms to extract relevant information from the input domains to answer a certain question. Thus, robust attention mechanisms are essential for powerful VQA models. In this paper, we propose a recurrent attention mechanism and show its benefits compared to the traditional convolutional approach. We perform two ablation studies to evaluate recurrent attention. 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. Secondly, we design an architecture for VQA which utilizes dual (textual and visual) Recurrent Attention Units (RAUs). Using this model, we show the effect of all possible combinations of recurrent and convolutional dual attention. Our single model outperforms the first place winner on the VQA 2016 challenge and to the best of our knowledge, it is the second best performing single model on the VQA 1.0 dataset. Furthermore, our model noticeably improves upon the winner of the VQA 2017 challenge. Moreover, we experiment replacing attention mechanisms in state-of-the-art models with our RAUs and show increased performance.