In the fashion industry, many tasks require human-level cognitive skills, such as detecting similar products or identifying facets in products like sleeve length or silhouette types in dresses. Pau Carré explains how Gilt is reshaping the fashion industry by leveraging the power of deep learning and GPUs to address these challenges.
Gilt is building automated faceting systems to detect dresses based on their silhouette, neckline, sleeve type, and occasion. On top of that, it is also developing systems to detect dress similarity, which can be useful for product recommendations. When integrated with automated faceting, customers will be able to find similar products with different facets. (For instance, a customer might be very interested in a particular dress but wants a different neckline or sleeve length.)
Pau Carré is a deep learning software engineer at Gilt. Pau has 10 years of experience encompassing software security, IT management, microwave networks profiling, quality engineering, and deep learning and functional programming for the fashion industry. Over his career, he has lived and worked in cosmopolitan Barcelona, paradisiac Mallorca, and magnificent Vienna and is now based in welcoming Dublin.
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