Summary of Exploring the Potentials and Challenges Of Deep Generative Models in Product Design Conception, by Phillip Mueller et al.
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
by Phillip Mueller, Lars Mikelsons
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the reasons behind the limited application of Deep Generative Models (DGMs) in synthesizing product design concepts. Despite their promise in automating and streamlining manual iterations, DGMs have yet to be widely adopted. The authors analyze five DGM families – VAE, GAN, Diffusion, Transformer, Radiance Field – assessing their strengths, weaknesses, and general applicability for product design conception. This analysis aims to provide insights that simplify the decision-making process for engineers, helping them determine which method is most effective for their specific challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how deep learning methods can help with designing products. Right now, these methods aren’t being used much in this area because there are some big challenges to overcome. The authors look at five different types of these models and see what they’re good at and where they fall short. They want to make it easier for people who design products to decide which type of model is best for their project. |
Keywords
* Artificial intelligence * Deep learning * Diffusion * Gan * Transformer
