FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing

TL;DR

FASH-iCNN is a multimodal system trained on Vogue images that makes the cultural logic of fashion houses, eras, and color traditions inspectable, revealing which visual features encode editorial identity.

Contribution

It introduces a system that not only predicts fashion attributes but also makes the encoding of cultural logic transparent and interpretable.

Findings

Clothing-only model achieves 78.2% top-1 accuracy for fashion houses.

Texture and luminance are primary carriers of editorial identity.

Removing color minimally impacts house identification accuracy.

Abstract

Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN, a multimodal system trained on 87,547 Vogue runway images across 15 fashion houses spanning 1991-2024 that makes this cultural logic inspectable. Given a photograph of a garment, the system recovers which house produced it, which era it belongs to, and which color tradition it reflects. A clothing-only model identifies the fashion house at 78.2% top-1 across 14 houses, the decade at 88.6% top-1, and the specific year at 58.3% top-1 across 34 years with a mean error of just 2.2 years. Probing which visual channels carry this signal reveals a sharp dissociation: removing color costs only 10.6pp of house identity accuracy, while removing texture costs 37.6pp, establishing texture and luminance as the primary carriers of editorial identity. FASH-iCNN treats editorial culture as the signal rather than background noise, identifying which houses, eras, and color traditions shaped each output so that users can see not just what the system predicts but which houses, editors, and historical moments are encoded in that prediction.