DeepXplore google
Deep learning (DL) systems are increasingly deployed in security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system’s behavior for corner-case inputs are of great importance. However, systematic testing of large-scale DL systems with thousands of neurons and millions of parameters for all possible corner-cases is a hard problem. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose different erroneous behaviors for rare inputs. We present DeepXplore, the first whitebox framework for systematically testing real-world DL systems. We address two problems: (1) generating inputs that trigger different parts of a DL system’s logic and (2) identifying incorrect behaviors of DL systems without manual effort. First, we introduce neuron coverage for estimating the parts of DL system exercised by a set of test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles and thus avoid manual checking for erroneous behaviors. We demonstrate how finding inputs triggering differential behaviors while achieving high neuron coverage for DL algorithms can be represented as a joint optimization problem and solved efficiently using gradient-based optimization techniques. DeepXplore finds thousands of incorrect corner-case behaviors in state-of-the-art DL models trained on five popular datasets. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running on a commodity laptop. The inputs generated by DeepXplore achieved 33.2% higher neuron coverage on average than existing testing methods. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve classification accuracy or identify polluted training data. …

Grey Machine Learning google
A brief introduction to the Grey Machine Learning

Feedback Networks google
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iteration’s output. We establish that a feedback based approach has several fundamental advantages over feedforward: it enables making early predictions at the query time, its output naturally conforms to a hierarchical structure in the label space (e.g. a taxonomy), and it provides a new basis for Curriculum Learning. We observe that feedback networks develop a considerably different representation compared to feedforward counterparts, in line with the aforementioned advantages. We put forth a general feedback based learning architecture with the endpoint results on par or better than existing feedforward networks with the addition of the above advantages. We also investigate several mechanisms in feedback architectures (e.g. skip connections in time) and design choices (e.g. feedback length). We hope this study offers new perspectives in quest for more natural and practical learning models. …

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