Deep Learning Impact (DLI) google
Deep Learning Impact (DLI) is a set of software tools to help users develop AI models with the leading open source deep learning frameworks, like TensorFlow and Caffe, for the deployment and prediction phases of deep learning. DLI enables users to run distributed deep learning workloads on x86 and Power, and complements the PowerAI deep learning software distribution. …

Scalable Logo Self-co-Learning (SL^2) google
Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically, we propose a novel incremental learning approach, called Scalable Logo Self-co-Learning (SL^2), capable of automatically self-discovering informative training images from noisy web data for progressively improving model capability in a cross-model co-learning manner. Moreover, we introduce a very large (2,190,757 images of 194 logo classes) logo dataset ‘WebLogo-2M’ by an automatic web data collection and processing method. Extensive comparative evaluations demonstrate the superiority of the proposed SL^2 method over the state-of-the-art strongly and weakly supervised detection models and contemporary webly data learning approaches. …

Domain Generating Algorithm (DGA) google
Domain generation algorithm (DGA) are algorithms seen in various families of malware that are used to periodically generate a large number of domain names that can be used as rendezvous points with their controllers. The large number of potential rendezvous points makes it difficult for law enforcement to effectively shut down botnets since infected computers will attempt to contact some of these domain names every day to receive updates or commands. By using public-key cryptography, it is unfeasible for law enforcement and other actors to mimic commands from the malware controllers as some worms will automatically reject any updates not signed by the malware controllers. …

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