recent advances in deep learning groknet the computer vision topics we have explored so far are all related to single-task learning that iswe specifically design network with one loss function and desired outcome--classifying an image into distinct categories modern problems in the digital age have more complex requirements that need more holistic approach consider an ecommerce marketplace when user uploads picture to list product for salethey might not add detailed and comprehensive description about the product in most casesthe uploader would add one-line description and broad category for the product (which might not be exactly trueto understand the problem betterconsider sample product listing for chair"nice sturdy chair for salejust year old and condition like new this user-drafted description lacks lot of information that might be ideal for buyer to make more informed decision informative attributes that would have been ideal for the buyer (as well as the marketplacewould include the color of the chairthe chair' make and modelthe year of manufactureetc from an engineering point of viewranking such product listing against user-based search query for the feed would be difficult taskas it might not match most of the relevant information fields solution to this problem would be augmenting additional information through several individual computer vision tasks--for exampleone task to classify the image into broad category (furnituretools/vehicles/books/etc )and then another model to classify with more specificity within vertical (make and model year)and so on there might also arise need to cater individual models for each vertical of products--for exampleapparelsfurniturebooksetc considering the wide range of possibilitieswe might often face the challenge of building and maintaining hundreds of distinct models considering the facebook marketplace as problemthe company released grokneta singleunified model with full coverage across all products with unified modelthe company has been able to reduce