Product semantics translation from brain activity via adversarial learning
P Wang, Z Gong, S Wang, H Dong, J Fan, L Li… - arXiv preprint arXiv …, 2021 - arxiv.org
arXiv preprint arXiv:2103.15602, 2021•arxiv.org
A small change of design semantics may affect a user's satisfaction with a product. To modify
a design semantic of a given product from personalised brain activity via adversarial
learning, in this work, we propose a deep generative transformation model to modify product
semantics from the brain signal. We attempt to accomplish such synthesis: 1) synthesising
the product image with new features corresponding to EEG signal; 2) maintaining the other
image features that irrelevant to EEG signal. We leverage the idea of StarGAN and the …
a design semantic of a given product from personalised brain activity via adversarial
learning, in this work, we propose a deep generative transformation model to modify product
semantics from the brain signal. We attempt to accomplish such synthesis: 1) synthesising
the product image with new features corresponding to EEG signal; 2) maintaining the other
image features that irrelevant to EEG signal. We leverage the idea of StarGAN and the …
A small change of design semantics may affect a user's satisfaction with a product. To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal. We attempt to accomplish such synthesis: 1) synthesising the product image with new features corresponding to EEG signal; 2) maintaining the other image features that irrelevant to EEG signal. We leverage the idea of StarGAN and the model is designed to synthesise products with preferred design semantics (colour & shape) via adversarial learning from brain activity, and is applied with a case study to generate shoes with different design semantics from recorded EEG signals. To verify our proposed cognitive transformation model, a case study has been presented. The results work as a proof-of-concept that our framework has the potential to synthesis product semantic from brain activity.
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