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Image reconstruction and captioning from brain activity evoked by visual stimuli allow researchers to further understand the connection between the human brain and the visual perception system.
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Visual image reconstruction from human brain activity using a combination of multiscale local image decoders
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Bayesian reconstruction of natural images from human brain activity
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Generic decoding of seen and imagined objects using hierarchical visual features
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Generative adversarial networks for reconstructing natural images from brain activity
Katja Seeliger, Umut Güçlü, Luca Ambrogioni, Yagmur Güçlütürk, and Marcel AJ van Gerven · 2018
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From voxels to pixels and back: Self-supervision in natural-image reconstruction from fmri
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Language models are unsupervised multitask learners
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Generative modeling by estimating gradients of the data distribution
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Unsupervised learning of visual features by contrasting cluster assignments
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Denoising diffusion probabilistic models
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Reconstructing natural scenes from fmri patterns using bigbigan
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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A massive 7t fmri dataset to bridge cognitive neuroscience and artificial intelligence
Emily J Allen, Ghislain St-Yves, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan T Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, et al · 2022
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Coyo-700m: Image-text pair dataset
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Self-supervised natural image reconstruction and large-scale semantic classification from brain activity
Guy Gaziv, Roman Beliy, Niv Granot, Assaf Hoogi, Francesca Strappini, Tal Golan, and Michal Irani · 2022
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Decoding natural image stimuli from fmri data with a surface-based convolutional network
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Generation of viewed image captions from human brain activity via unsupervised text latent space
Saya Takada, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 2020
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Diffusion models beat gans on image synthesis
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A neural decoding algorithm that generates language from visual activity evoked by natural images
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Grad-tts: A diffusion probabilistic model for text-to-speech
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Learning transferable visual models from natural language supervision
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Mind reader: Reconstructing complex images from brain activities
Sikun Lin, Thomas Sprague, and Ambuj K Singh · 2022
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High-resolution image synthesis with latent diffusion models
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Photorealistic text-to-image diffusion models with deep language understanding
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Versatile diffusion: Text, images and variations all in one diffusion model
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A cnn-transformer hybrid approach for decoding visual neural activity into text
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High-resolution image reconstruction with latent diffusion models from human brain activity
Yu Takagi and Shinji Nishimoto · 2023
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Closest in time.