Summary of Mdiff: Exploiting Multimodal Score-based Diffusion Models For New Fashion Product Performance Forecasting, by Andrea Avogaro et al.
MDiFF: Exploiting Multimodal Score-based Diffusion Models for New Fashion Product Performance Forecasting
by Andrea Avogaro, Luigi Capogrosso, Franco Fummi, Marco Cristani
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes MDiFF, a novel pipeline for forecasting the performance of new fashion products. The fast fashion industry faces significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products could improve efficiency and resource utilization. However, existing deterministic models struggle with domain shift challenges when encountering items outside their training data distribution. MDiFF addresses this issue by simulating how new items are adopted using a continuous-time diffusion process. The pipeline consists of two steps: a score-based diffusion model predicts multiple future sales for different clothes over time, and then a lightweight Multi-layer Perceptron (MLP) refines these predictions to get the final forecast. MDiFF leverages the strengths of both architectures, resulting in the most accurate and efficient forecasting system for the fast-fashion industry at the state-of-the-art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MDiFF is a new way to predict how well new fashion products will sell. The fashion industry makes too many clothes and it’s bad for the environment. To fix this, we need to be able to guess how popular new clothes will be before they’re even made. This is hard because there isn’t much data about new things, and what works now might not work later. Some models are really good at guessing sales, but they get confused when they see something new that they didn’t learn about. MDiFF uses a special way to predict how people will like new clothes over time. It’s like imagining how popular a new fashion trend will be before it even happens. This helps us make better guesses and use fewer resources. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
