Recovering Localized Adversarial Attacks

Göpfert JP, Wersing H, Hammer B. Recovering Localized Adversarial Attacks. In: Tetko IV, Kůrková V, Karpov P, Theis F, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019: 302-311.Deep convolutional neural networks have achieved great successes over recent years, particularly in the domain of computer vision. They are fast, convenient, and – thanks to mature frameworks – relatively easy to implement and deploy. However, their reasoning is hidden inside a black box, in spite of a number of proposed approaches that try to provide human-understandable explanations for the predictions of neural networks. It is still a matter of debate which of these explainers are best suited for which situations, and how to quantitatively evaluate and compare them [1]. In this contribution, we focus on the capabilities of explainers for convolutional deep neural networks in an extreme situation: a setting in which humans and networks fundamentally disagree. Deep neural networks are susceptible to adversarial attacks that deliberately modify input samples to mislead a neural network’s classification, without affecting how a human observer interprets the input. Our goal with this contribution is to evaluate explainers by investigating whether they can identify adversarially attacked regions of an image. In particular, we quantitatively and qualitatively investigate the capability of three popular explainers of classifications – classic salience, guided backpropagation, and LIME – with respect to their ability to identify regions of attack as the explanatory regions for the (incorrect) prediction in representative examples from image classification. We find that LIME outperforms the other explainers

Scaffolding Haptic Attention with Controller Gating

Moringen A, Fleer S, Ritter H. Scaffolding Haptic Attention with Controller Gating. In: Tetko IV, Kůrková V, Karpov P, Theis F, eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation.28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Vol 11727. Cham: Springer; 2019: 669-684.A powerful concept that emerged within the field of educational psychology is scaffolding. Characterizing favourable expert-learner interaction, it can be defined as a temporal support that provides a novice an adaptable guidance to either learn tasks that would usually be beyond own capabilities or to speed up and refine the learning of manageable problems. In this work we apply the above-mentioned concept to implement a novel multi-strategy haptic exploration controller that is able to perform object identification using a robot. In our previous work we have proposed a reinforcement learner that acquires haptic exploration capabilities for a goal-directed task by optimizing motor control in a strongly restricted attentional framework, called the haptic attention model (HAM). The resulting policy however was not characterized by a smooth energy-efficient exploration suitable for execution on a robot. In this work, we scaffold the designed learning architecture by imposing the so-called controller gating that is trained to switch between orientation and position control. Integrated in the same reinforcement learning setting as the HAM, controller gating guides and monitors the data acquisition. Inspired by the human expert scaffolding, it analyzes the HAM internal data representation, modulates the HAM weight update process, and forces data acquisition that achieves efficient and successful completion of the goal. Our computational scaffold adapts to the learner model, while it masters the skill. The evaluation demonstrated that it is more likely for the trained model to change either location or orientation than simultaneously change both, which significantly improves the smoothness and the energy-efficiency of the resulting exploration

Short Text Categorization using World Knowledge

The content of the World Wide Web is drastically multiplying, and thus the amount of available online text data is increasing every day. Today, many users contribute to this massive global network via online platforms by sharing information in the form of a short text. Such an immense amount of data covers subjects from all the existing domains (e.g., Sports, Economy, Biology, etc.). Further, manually processing such data is beyond human capabilities. As a result, Natural Language Processing (NLP) tasks, which aim to automatically analyze and process natural language documents have gained significant attention. Among these tasks, due to its application in various domains, text categorization has become one of the most fundamental and crucial tasks. However, the standard text categorization models face major challenges while performing short text categorization, due to the unique characteristics of short texts, i.e., insufficient text length, sparsity, ambiguity, etc. In other words, the conventional approaches provide substandard performance, when they are directly applied to the short text categorization task. Furthermore, in the case of short text, the standard feature extraction techniques such as bag-of-words suffer from limited contextual information. Hence, it is essential to enhance the text representations with an external knowledge source. Moreover, the traditional models require a significant amount of manually labeled data and obtaining labeled data is a costly and time-consuming task. Therefore, although recently proposed supervised methods, especially, deep neural network approaches have demonstrated notable performance, the requirement of the labeled data remains the main bottleneck of these approaches. In this thesis, we investigate the main research question of how to perform \textit{short text categorization} effectively \textit{without requiring any labeled data} using knowledge bases as an external source. In this regard, novel short text categorization models, namely, Knowledge-Based Short Text Categorization (KBSTC) and Weakly Supervised Short Text Categorization using World Knowledge (WESSTEC) have been introduced and evaluated in this thesis. The models do not require any hand-labeled data to perform short text categorization, instead, they leverage the semantic similarity between the short texts and the predefined categories. To quantify such semantic similarity, the low dimensional representation of entities and categories have been learned by exploiting a large knowledge base. To achieve that a novel entity and category embedding model has also been proposed in this thesis. The extensive experiments have been conducted to assess the performance of the proposed short text categorization models and the embedding model on several standard benchmark datasets