We consider the problem of complementary fashion prediction. Existing approaches focus on learning an embedding space where fashion items from different categories that are visually compatible are closer to each other. However, creating such labeled outfits is intensive and also not feasible to generate all possible outfit combinations, especially with large fashion catalogs. In this work, we propose a semi-supervised learning approach where we leverage large unlabeled fashion corpus to create pseudo positive and negative outfits on the fly during training. For each labeled outfit in a training batch, we obtain a pseudo-outfit by matching each item in the labeled outfit with unlabeled items. Additionally, we introduce consistency regularization to ensure that representation of the original images and their transformations are consistent to implicitly incorporate colour and other important attributes through self-supervision. We conduct extensive experiments on Polyvore, Polyvore-D and our newly created large-scale Fashion Outfits datasets, and show that our approach with only a fraction of labeled examples performs on-par with completely supervised methods. CCS CONCEPTS• Computing methodologies → Semi-supervised learning settings; Neural networks.

Recommender systems based on collaborative filtering are highly vulnerable to data poisoning attacks, where a determined attacker injects fake users with false user-item feedback, with an objective to either corrupt the recommender system or promote/demote a target set of items. Recently, differential privacy was explored as a defense technique against data poisoning attacks in the typical machine learning setting. In this paper, we study the effectiveness of differential privacy against such attacks on matrix factorization based collaborative filtering systems. Concretely, we conduct extensive experiments for evaluating robustness to injection of malicious user profiles by simulating common types of shilling attacks on real-world data and comparing the predictions of typical matrix factorization with differentially private matrix factorization.

Microscopic polyangiitis (MPA) is an antineutrophil cytoplasmic autoantibody-associated vasculitis, usually affecting the small vessels in the form of systemic necrotizing vasculitis. It commonly manifests as diffuse alveolar hemorrhage and rapidly progressive glomerulonephritis but may present with the involvement of multiple organs. Timely diagnosis at an early localized stage is crucial for instituting an early disease-specific treatment. We report a case of a 63-year-old female who was diagnosed with MPA predominantly involving lungs and middle ears. The absence of a typical pulmonary–renal presentation and clinical features favoring obstructive airway disease and tuberculosis led to a delayed diagnosis. The presence of antimyeloperoxidase antibodies in high titer and a clinical response to monoclonal antibody therapy, thereby confirming the diagnosis of MPA prompted us to report this case.