Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟯𝟱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Microsoft Researchers Unlock New Avenues In Image-Generation Research With Manifold Matching Via Metric Learning Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗠𝗮𝗻𝗶𝗳𝗼𝗹𝗱 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝘃𝗶𝗮 𝗗𝗲𝗲𝗽 𝗠𝗲𝘁𝗿𝗶𝗰 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 🔸 This paper is published arxiv 2021. 🔸 By developing fresh images, generative image models provide a distinct value. These photos could be clear super-resolution copies of current images or even manufactured shots that look realistic. The framework of training two networks against each other has shown pioneering success with Generative Adversarial Networks (GANs) and their variants: a generator network learns to generate realistic fake data that can fool a discriminator network, and the discriminator network learns to correctly tell apart the generated counterfeit data from the actual data. 🔹Microsoft researchers offer a novel framework for generative models called Manifold Matching via Metric Learning in a recent paper titled “Manifold Matching via Deep Metric Learning for Generative Modeling” (MvM). ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator. In our framework, we view the real data set as some manifold embedded in a high-dimensional Euclidean space. The distribution generator aims at generating samples that follow some distribution condensed around the real data manifold. It is achieved by matching two sets of points using their geometric shape descriptors, such as centroid and p-diameter, with learned distance metric; the metric generator utilizes both real data and generated samples to learn a distance metric which is close to some intrinsic geodesic distance on the real data manifold. The produced distance metric is further used for manifold matching. The two networks are learned simultaneously during the training process. We apply the approach on both unsupervised and supervised learning tasks: in unconditional image generation task, the proposed method obtains competitive results compared with existing generative models; in super-resolution task, we incorporate the framework in perception-based models and improve visual qualities by producing samples with more natural textures. Experiments and analysis demonstrate the feasibility and effectiveness of the proposed framework. ------------------------------------------------------------------- #computervision #artificialintelligence #innovation -------------------------------------------------------------------