Image retrieval systems allow individuals to find images that are semantically similar to a query image. This serves as the backbone of reverse image search engines and many product recommendation engines. Restricting an image retrieval system to particular subsets of images can yield new insights into relationships in the visual world.<\/p>\n
In this webinar, Microsoft Research Development Engineer\u202fMark Hamilton\u202fpresents a novel method for specializing image retrieval systems called conditional image retrieval. When applied over large art datasets in particular, conditional image retrieval provides visual analogies that bring to light hidden connections among different artists, cultures, and media. This aims to encourage a new level of engagement with creative artifacts and inspire people to imagine new works of art. Hamilton will demonstrate how conditional image retrieval systems can efficiently find shared semantics between works of vastly different media and cultural origin. He\u2019ll also demonstrate how this approach can improve generative adversarial networks (GANs), algorithms that create novel images from scratch, by identifying where GANs fail to model the true data distribution. Finally, Hamilton will introduce a generalization of Additive Shapley Values (SHAP) for better understanding why neural networks view images as similar.<\/p>\n
Together, you\u2019ll learn how to:<\/p>\n
Resource list:<\/strong><\/p>\n *This on-demand webinar features a previously recorded Q&A session and open captioning<\/p>\n\n