To make this image translation effective, we assume that the appearance of an image depends on three factors: the content, the domain and the style. The proposed translator is based on an encoder, which extracts domain-invariant intermediate features, and a decoder, which projects these features onto the domain-specific target distribution. The translator network is “universal” because it is not specific for a given source dataset but can transform images from multiple source domains into the target domain, given a domain label as input (Fig. In more detail, our goal is to build and train a “universal” translator which can transform an image from an input domain to a target domain. While this strategy has been recently adopted in the single-source UDA scenario , we are the first to show how it can be effectively used in a MSDA setting. Then, the synthetically generated images are used for training the target classifier. Specifically, we generate artificial target samples by “translating” images from all the source domains into target-like images. In this paper we deal with (unsupervised) MSDA using a data-augmentation approach based on a generative adversarial network (GAN) . However, although more data can be used, MSDA is challenging as multiple domain-shift problems need to be simultaneously and coherently solved. In this case, multi-source domain adaptation (MSDA) methods may be adopted, in which more than one source dataset is considered in order to make the adaptation process more robust. While most previous adaptation approaches consider a single-source domain, in real-world applications we may have access to multiple datasets. In many practical scenarios, the target data are not annotated and unsupervised domain adaptation (UDA) methods are required. Since the two domains typically have different marginal feature distributions, the adaptation process needs to reduce the corresponding domain shift .
Visit smashingsecurity.A well-known problem in computer vision is the need to adapt a classifier trained on a given source domain in order to work on a different target domain. Industry-leading Offensive Security provides training for your organization designed by the same minds behind Kali Linux and the OSCP. With the skills gap increasing, it’s more important than ever to train your staff effectively and efficiently. Get exclusive perks like 1Password swag for attending events, enjoy the chance to network with top security leaders, and much much more. Learn from security experts at top organizations, hear about sizzling security trends, and get quick tips for building a culture of security at home and work.
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Windows “PetitPotam” network attack – how to protect against it - Naked Security.PetitPotam proof-of-concept tool - GitHub.
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