The technological advances happening in data analytics can be hard to keep on top of - and we have to admit they can be difficult to understand if you’re not interacting with these types of solutions regularly. We often get asked to explain what exactly powers our fashion analytics - how exactly is it able to tell the difference between a puffer and a peacoat? Today we’re talking a little more about deep learning - one of the biggest advances in machine learning that is making our fashion ‘eye’ that much smarter and accurate.
Deep learning represents the biggest trend in machine learning in recent years, and for good reason, since it's making a huge impact in different technological areas, such as computer vision or natural language processing. It can be defined as a family of algorithms with the ability to learn on their own how to answer any specific question, without being explicitly programmed. All these algorithms require to learn is data, since they learn by the example. If we can provide good quality data containing enough instances of the task to solve, deep learning algorithms can learn the relationship of answer to question automatically. For example, a deep learning system aimed to identify items of clothing in pictures will need to be fed images that are labeled as such, nothing else.
From a technical perspective, deep learning algorithms exploit many layers of information processing, being able to automatically create a hierarchy of features directly from raw data. The more data we provide these algorithms to define the hierarchy, the more general the features they can define to solve a given problem, and therefore, the more useful such features will be when dealing with new, yet similar problems. This ability to automatically transform raw unstructured data to meaningful features has made deep learning a game changer in problems related to the analysis of images, video, and text.
Deep learning is currently defining the state of the art in data analytics, and this is having a transformational impact on e-commerce business in general and on the fashion industry in particular. Today we can extract insights and structured information from all kind of fashion data sources like never before, and this is modelling the future of the industry. To what extent you may be wondering? Retail giant Amazon is organizing an annual conference workshop to specifically explore the problems, applications, and future directions of machine learning applied to the fashion domain, where deep learning is playing a leading role. Just in the last edition of this workshop, deep learning was the approach selected to tackle problems such as style discovery in pictures, fashion traits prediction, fashion attributes learning, or clothes image segmentation.
At StyleSage, we have deployed the major deep learning algorithms, and they are key in our data analytics pipeline. We have collected, processed and curated terabytes of data over the years, which has enabled us to obtain and extract a rich level of detail around fashion features - that is trained on millions of examples. Today, our deep learning systems analyze different types of unstructured fashion data (text, images, time series) on a daily basis and provide us with the insights upon which our customers make million-dollar assortment decisions. Just for our images, currently we have deep learning models detecting types of clothing, assessing images similarity, extracting fashion attributes, analysing patterns and colors, segmenting scenes… and that is just for starters, the best is yet to come.
You just went deep - now don’t you look smart. Learn more about our solution here.