Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space
Summary
Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space is a scholarly article[1].
Key Facts
Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space. Retrieved May 24, 2026, from https://4ort.xyz/entity/hyperrealistic-neural-decoding-for-reconstructing-faces-from-fmri-activations-via-the-gan-latent-space
MLA“Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/hyperrealistic-neural-decoding-for-reconstructing-faces-from-fmri-activations-via-the-gan-latent-space.
BibTeX@misc{4ortxyz_hyperrealistic-neural-decoding-for-reconstructing-faces-from-fmri-activations-via-the-gan-latent-space_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space}}, year = {2026}, url = {https://4ort.xyz/entity/hyperrealistic-neural-decoding-for-reconstructing-faces-from-fmri-activations-via-the-gan-latent-space}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Hyperrealistic neural decoding for reconstructing faces from fMRI activations via the GAN latent space — https://4ort.xyz/entity/hyperrealistic-neural-decoding-for-reconstructing-faces-from-fmri-activations-via-the-gan-latent-space (retrieved 2026-05-24)
