Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation

Yulan Chen

ICIP 2022 · first public 2022-01-01

paper ↗catalogued✓ abstract read

In one paragraph

Proposes an Attention-based Dataset Distillation Graph Neural Network (ADD-GNN) that distills knowledge from designer-generated outfit data, rather than noisy user-generated social-media data, to guide feature representation learning, and models overall outfit compatibility with an attention-based graph neural network jointly optimized with the distillation component.

Where it sits

Setting: Image classification

BibTeX (generated; prefer the venue's official entry)

@article{chen2022learning,
  title   = {Learning from Designers: Fashion Compatibility Analysis Via Dataset Distillation},
  author  = {Yulan Chen},
  journal = {ICIP 2022},
  year    = {2022}
}