# DeSIGN: Design Inspiration from Generative Networks

arXiv: https://arxiv.org/abs/1804.00921 · HTML: https://mlsanity.com/paper/1804.00921/

## Key facts

- Cited by 20 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 20 of 20)

- [DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models](https://mlsanity.com/paper/2302.06826/) (2023)
- [ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints](https://mlsanity.com/paper/2308.02669/) (2023)
- [Creativity and Machine Learning: A Survey](https://mlsanity.com/paper/2104.02726/) (2021)
- [DVM-CAR: A large-scale automotive dataset for visual marketing research and applications](https://mlsanity.com/paper/2109.00881/) (2021)
- [Can AI Be as Creative as Humans?](https://mlsanity.com/paper/2401.01623/) (2024)
- [PartCraft: Crafting Creative Objects by Parts](https://mlsanity.com/paper/2407.04604/) (2024)
- [Enhancing Creative Generation on Stable Diffusion-based Models](https://mlsanity.com/paper/2503.23538/) (2025)
- [CIZSL++: Creativity Inspired Generative Zero-Shot Learning](https://mlsanity.com/paper/2101.00173/) (2021)
- [DreamCreature: Crafting Photorealistic Virtual Creatures from Imagination](https://mlsanity.com/paper/2311.15477/) (2023)
- [From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges](https://mlsanity.com/paper/2402.12702/) (2024)
- [What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design](https://mlsanity.com/paper/2406.13436/) (2024)
- [VLM-Guided Adaptive Negative Prompting for Creative Generation](https://mlsanity.com/paper/2510.10715/) (2025)
- [Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation](https://mlsanity.com/paper/2104.09757/) (2021)
- [Continual Zero-Shot Learning through Semantically Guided Generative Random Walks](https://mlsanity.com/paper/2308.12366/) (2023)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [The Impact of Generative AI on the Future of Visual Content Marketing](https://mlsanity.com/paper/2211.12660/) (2022)
- [Creative Agents: Simulating the Systems Model of Creativity with Generative Agents](https://mlsanity.com/paper/2411.17065/) (2024)
- [Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models](https://mlsanity.com/paper/2403.19620/) (2024)
- [Unrolled Creative Adversarial Network For Generating Novel Musical Pieces](https://mlsanity.com/paper/2501.00452/) (2024)
- [Handloom Design Generation Using Generative Networks](https://mlsanity.com/paper/2505.14330/) (2025)

## Abstract (arXiv)

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 protocol associating automatic metrics and human experimental studies that we hope will help ease future research. We show that our proposed creativity criterion yield better overall appreciation than the one employed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelty and likability.