Collaborating Authors


How to Build an AI Fashion Designer


This is a write-up for my old project ClothingGAN. The project generates clothing design with AI using StyleGAN and semantically edits it with attributes such as sleeve, size, dress, jacket, etc. You can also do style transfer as shown in the image above by first generating 2 different clothing designs (output 1) with different seed numbers. Then, it will generate a third design (output 2) that mixes the previous 2 designs. You can then adjust how much style or structure you want it to inherit from the two original designs.


AAAI Conferences

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of 24,752 labeled images on fashion conversations, containing visual and textual cues, available for the research community.

How Technology Is Reshaping The Fashion Industry - fashionabc


Estimated to be worth $3T by the end of the decade, per CB Insights' Industry Analyst Consensus, the fashion industry is growing at a fast pace, led by cutting-edge technologies. From robots that sew and cut fabric to AI algorithms that predict style trends, VR mirrors in dressing rooms, shopping off the runway and a number of other innovations show how technology is automating and evolving the industry. In 2016, Google collaborated with online fashion platform Zalando and production company Stinkdigital to launch predictive design engine, Project Muze. The algorithm consisted of a set of aesthetic parameter and trained a neural network to comprehend colours, textures and styles derived from Google Fashion Trends Report and data sourced by Zalando -- to create designs in sync with with style preferences identified by the network. Amazon is taking an algorithmic approach to fashion as well.

Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning Artificial Intelligence

Real economies can be seen as a sequential imperfect-information game with many heterogeneous, interacting strategic agents of various agent types, such as consumers, firms, and governments. Dynamic general equilibrium models are common economic tools to model the economic activity, interactions, and outcomes in such systems. However, existing analytical and computational methods struggle to find explicit equilibria when all agents are strategic and interact, while joint learning is unstable and challenging. Amongst others, a key reason is that the actions of one economic agent may change the reward function of another agent, e.g., a consumer's expendable income changes when firms change prices or governments change taxes. We show that multi-agent deep reinforcement learning (RL) can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types, in economic simulations with many agents, through the use of structured learning curricula and efficient GPU-only simulation and training. Conceptually, our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing, that are commonly used for analytical tractability. Our GPU implementation enables training and analyzing economies with a large number of agents within reasonable time frames, e.g., training completes within a day. We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes. We validate the learned meta-game epsilon-Nash equilibria through approximate best-response analyses, show that RL policies align with economic intuitions, and that our approach is constructive, e.g., by explicitly learning a spectrum of meta-game epsilon-Nash equilibria in open RBC models.

Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry Artificial Intelligence

The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.

Artificial intelligence in the fashion industry


Research being carried out by a research team around Professor Ohbyung Kwon at Kyung Hee University and Dr Christine (Eunyoung) Sung at Jake Jabs College of Business and Entrepreneurship, Montana State University, involves examining consumers' evaluations of fashion products designed using generative adversarial networks (GANs), an Artificial Intelligence (AI) technology. They analyse consumers' buying behaviour and offer practical advice for businesses that are considering using GANs to develop products for the retail fashion market. Artificial Intelligence (AI) technology is changing the retail landscape. Generative AI is being used to produce creative outputs; tasks that have traditionally been considered exclusive to humans. In particular, generative adversarial networks (GANs), an Artificial Intelligence technology, powerful machine learning models that can generate realistic images, videos, and voice outputs, are successfully performing creative tasks previously considered unique to humans.

fAshIon after fashion: A Report of AI in Fashion Artificial Intelligence

In this independent report fAshIon after fashion, we examine the development of fAshIon (artificial intelligence (AI) in fashion) and explore its potentiality to become a major disruptor of the fashion industry in the near future. To do this, we investigate AI technologies used in the fashion industry through several lenses. We summarise fAshIon studies conducted over the past decade and categorise them into seven groups: Overview, Evaluation, Basic Tech, Selling, Styling, Design, and Buying. The datasets mentioned in fAshIon research have been consolidated on one GitHub page for ease of use. We analyse the authors' backgrounds and the geographic regions treated in these studies to determine the landscape of fAshIon research. The results of our analysis are presented with an aim to provide researchers with a holistic view of research in fAshIon. As part of our primary research, we also review a wide range of cases of applied fAshIon in the fashion industry and analyse their impact on the industry, markets and individuals. We also identify the challenges presented by fAshIon and suggest that these may form the basis for future research. We finally exhibit that many potential opportunities exist for the use of AI in fashion which can transform the fashion industry embedded with AI technologies and boost profits.

Fashion Industry Showing More Imagination in Use of AI - AI Trends


The fashion industry did $3 trillion in business, 2% of global GDP in 2018; e-commerce fashion amounted to $520 billion in 2019. AI is poised to revolutionize the fashion industry by providing insights into fashion trends, purchase patterns, and enabling better inventory management. The global brand H&M has been applying AI solutions to boost business operations. One example is a system to organize and allocate masses of unsold stock to retail stories with highest demand, reducing the need for discounted sales. This is achieved by optimizing the supply chain and inventory management, reducing the amount of wasted clothing.

This ugly t-shirt makes you invisible to facial recognition tech


In William Gibson's novel Zero History, a key character dons the ugliest T-shirt in the world – a ridiculous-looking garment that magically renders the wearer invisible to CCTV. Now, as states across the world deploy artificially intelligent surveillance systems to track, trace and monitor citizens, we may find ourselves wearing ugly T-shirts of our own. Researchers at Northeastern University, MIT and IBM have designed a top printed with a kaleidoscopic patch of colour that renders the wearer undetectable to AI. It's part of a growing number of "adversarial examples" – physical objects designed to counteract the creep of digital surveillance. "The adversarial T-shirt works on the neural networks used for object detection," explains Xue Lin, an assistant professor of electrical and computer engineering at Northeastern, and co-author of a recent paper on the subject.

Five Weird and Hilarious Data Science Use Cases


Think about it – what's the first thought that comes to your mind when you think about Data Science? Here's what I'm proposing – how about we explore the road less traveled? In this weird and wacky world, our data science work reflects surprising connections, such as – if you are buying diapers then you are most likely to buy beer. Or, people who go to bars are a higher credit risk! Oh, I cannot miss out on this one – Smart people prefer curly fries.