Immunoproliferative small intestinal disease (IPSID) is a rare lympho-proliferative disorder of the upper small intestine. It is considered a special form of MALT lymphoma with propensity to malignant transformation. This disorder is rare in pediatric literature. We report a case of IPSID in a 16-year-old boy with low-grade malignant transformation, presenting as severe malnutrition and a possible association with Helicobacter pylori. The patient responded well to an extended treatment with tetracycline and eradication of H. pylori.
StyleGAN is arguably one of the most intriguing and well-studied generative models, demonstrating impressive performance in image generation, inversion, and manipulation. In this work, we explore the recent StyleGAN3 architecture, compare it to its predecessor, and investigate its unique advantages, as well as drawbacks. In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery. Next, our analysis of the disentanglement of the different latent spaces of StyleGAN3 indicates that the commonly used W/W+ spaces are more entangled than their StyleGAN2 counterparts, underscoring the benefits of using the StyleSpace for fine-grained editing. Considering image inversion, we observe that existing encoder-based techniques struggle when trained on unaligned data. We therefore propose an encoding scheme trained solely on aligned data, yet can still invert unaligned images. Finally, we introduce a novel video inversion and editing workflow that leverages the capabilities of a fine-tuned Style-GAN3 generator to reduce texture sticking and expand the field of view of the edited video. Code is available on our project page: https://yuval-alaluf.github.io/ stylegan3-editing/.* Denotes equal contribution.
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