TL;DR
This paper explores the use of various generative network architectures and novel loss functions inspired by Sharma-Mittal divergence to create original fashion designs, with an evaluation protocol combining automatic metrics and human studies.
Contribution
It introduces a new framework for fashion design generation using diverse GAN architectures and a novel loss function based on Sharma-Mittal divergence, along with an evaluation protocol.
Findings
61% of generated images are perceived as human-created
Proposed loss functions outperform existing ones in novelty and likability
Generated designs show high originality and human appreciation
Abstract
Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from noise vectors to generate fashion items, (ii) novel loss functions that encourage novelty, inspired from Sharma-Mittal divergence, a generalized mutual information measure for the widely used relative entropies such as Kullback-Leibler, and (iii) a generation process following the key elements of fashion design (disentangling shape and texture components). A key challenge of this study is the evaluation of generated designs and the retrieval of best ones, hence we put together an evaluation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
