Multi-Task Deep Learning For Image Tagging

Abstract: A fundamental characteristic of human learning is that we learn multiple pieces of information simultaneously. We can describe an image verbally because we are natural multi-task agents. A comparable concept in machine learning is called multi-task learning (MTL), and it has become increasingly useful in practice. A common MTL use case is image tagging. For example, a retailer can use MTL to identify visual attributes for clothing items. Multiple attributes are learned simultaneously such as the type of clothing, texture, color, pattern, gender and fit type. The tagged results can be used for customer profile analysis to make purchase recommendations. With a set of personal photos, it is possible to infer the fashion style of the shopper by analyzing the attributes of clothes and then recommend other clothing items for purchase. Tagging can also be used for retrieval systems like image
search, or as part of feature engineering. In this presentation, we build a multi-task deep learning model using DLPy to tag fashion clothing items.
Convolutional neural networks show extraordinary performance for image classification and object recognition applications. DLPy is a high-level and easy-to-use Python API for SASĀ® deep learning models. We explain how DLPy can be applied to data preparation, data processing, multi-task model building,
assessment and deployment for image tagging.

Bio: Wayne Thompson is the Manager of Data Science Technologies at SAS. One of the early pioneers of business predictive analytics, he is a globally recognized presenter, teacher, practitioner and innovator in the field of predictive analytics technology. He has worked alongside the world's biggest and most challenging companies to help them harness analytics to build high-performing organizations. Over the course of his 20-year career at SAS, he has been credited with bringing to market landmark SAS analytic technologies. His current focus initiatives include easy-to-use, self-service data mining tools for business analysts, deep learning and cognitive computing.Thompson received his MS and PhD degrees from the University of Tennessee. During his PhD program, he was also a visiting scientist at the Institut Superieur d'Agriculture de Lille in Lille, France.