BigGAN-based Bayesian reconstruction of natural images from human brain activity

• URL: http://arxiv.org/abs/2003.06105v1
• Date: Fri, 13 Mar 2020 04:32:11 GMT
• Title: BigGAN-based Bayesian reconstruction of natural images from human brain activity
• Authors: Kai Qiao, Jian Chen, Linyuan Wang, Chi Zhang, Li Tong, Bin Yan
• Abstract summary: We propose a new GAN-based visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data. GAN-BVRM employs the pre-trained generator of the prevailing BigGAN to generate masses of natural images. Experimental results revealed that GAN-BVRM improves the fidelity and naturalness, that is, the reconstruction is natural and similar to the presented image stimuli.
• Score: 14.038605815510145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is difficult, especially when reconstructing viewed natural images. Visual reconstruction is a conditional image generation on fMRI data and thus generative adversarial network (GAN) for natural image generation is recently introduced for this task. Although GAN-based methods have greatly improved, the fidelity and naturalness of reconstruction are still unsatisfactory due to the small number of fMRI data samples and the instability of GAN training. In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images. GAN-BVRM employs the pre-trained generator of the prevailing BigGAN to generate masses of natural images, and selects the images that best matches with the corresponding brain activity through the encoding models as the reconstruction of the image stimuli. In this process, the semantic and detailed contents of reconstruction are controlled by decoded categories and encoding models, respectively. GAN-BVRM used the Bayesian manner to avoid contradiction between naturalness and fidelity from current GAN-based methods and thus can improve the advantages of GAN. Experimental results revealed that GAN-BVRM improves the fidelity and naturalness, that is, the reconstruction is natural and similar to the presented image stimuli.

Related papers

• In-Domain GAN Inversion for Faithful Reconstruction and Editability [132.68255553099834]
  We propose in-domain GAN inversion, which consists of a domain-guided domain-regularized and a encoder to regularize the inverted code in the native latent space of the pre-trained GAN model. We make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property.
  arXiv  Detail & Related papers  (2023-09-25T08:42:06Z)
• Natural scene reconstruction from fMRI signals using generative latent diffusion [1.90365714903665]
  We present a two-stage scene reconstruction framework called Brain-Diffuser'' In the first stage, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Vari Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model conditioned on predicted multimodal (text and visual) features.
  arXiv  Detail & Related papers  (2023-03-09T15:24:26Z)
• BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus Decoding [51.911473457195555]
  BrainCLIP is a task-agnostic fMRI-based brain decoding model. It bridges the modality gap between brain activity, image, and text. BrainCLIP can reconstruct visual stimuli with high semantic fidelity.
  arXiv  Detail & Related papers  (2023-02-25T03:28:54Z)
• Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images [79.07468367923619]
  We develop convolutional neural generative coding (Conv-NGC) We implement a flexible neurobiologically-motivated algorithm that progressively refines latent state maps. We study the effectiveness of our brain-inspired neural system on the tasks of reconstruction and image denoising.
  arXiv  Detail & Related papers  (2022-11-22T06:42:41Z)
• Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency [5.705640492618758]
  We propose a new framework to reconstruct facial images from fMRI data. The proposed framework accomplishes two goals: (1) reconstructing clear facial images from fMRI data and (2) maintaining the consistency of semantic characteristics.
  arXiv  Detail & Related papers  (2022-07-03T11:18:35Z)
• Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity [0.22940141855172028]
  We present a novel approach for enhanced image reconstruction, in which existing methods for object decoding and image reconstruction are merged together. This is achieved by conditioning the reconstructed image to its decoded image category using a class-conditional generative adversarial network and neural style transfer. The results indicate that our approach improves the semantic similarity of the reconstructed images and can be used as a general framework for enhanced image reconstruction.
  arXiv  Detail & Related papers  (2022-05-27T18:09:07Z)
• Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs [1.6904374000330984]
  We use an Instance-Conditioned GAN (IC-GAN) model to reconstruct images from fMRI patterns with both accurate semantic attributes and preserved low-level details. We trained ridge regression models to predict instance features, noise vectors, and dense vectors of stimuli from corresponding fMRI patterns. Then, we used the IC-GAN generator to reconstruct novel test images based on these fMRI-predicted variables.
  arXiv  Detail & Related papers  (2022-02-25T13:51:00Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation [181.08127307338654]
  This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. The deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images.
  arXiv  Detail & Related papers  (2020-03-30T17:45:07Z)
• Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN [2.0754848504005583]
  We employ a recently proposed large-scale bi-directional adversarial network, called BigBiGAN, to decode and reconstruct natural scenes from fMRI patterns. We computed a linear mapping between fMRI data, acquired over images from 150 different categories of ImageNet, and their corresponding BigBiGAN latent vectors. We applied this mapping to the fMRI activity patterns obtained from 50 new test images from 50 unseen categories in order to retrieve their latent vectors, and reconstruct the corresponding images.
  arXiv  Detail & Related papers  (2020-01-31T10:46:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.