Reconstructing perceived faces from brain activations with deep adversarial neural decoding
Authors: Yağmur Güçlütürk, Umut Güçlü, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel A. J. van Gerven
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our approach with a functional magnetic resonance imaging experiment and show that it can generate state-of-the-art reconstructions of perceived faces from brain activations. |
| Researcher Affiliation | Academia | Ya˘g mur Güçlütürk*, Umut Güçlü*, Katja Seeliger, Sander Bosch, Rob van Lier, Marcel van Gerven, Radboud University, Donders Institute for Brain, Cognition and Behaviour Nijmegen, the Netherlands {y.gucluturk, u.guclu}@donders.ru.nl |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. It mentions that its fMRI dataset is 'available from the first authors on reasonable request,' but this refers to data, not code. |
| Open Datasets | Yes | Celeb A dataset [48]. This dataset comprises 202599 in-the-wild portraits of 10177 people, which were drawn from online sources. |
| Dataset Splits | Yes | In total, 700 faces were presented twice for the training set, and 48 faces were repeated 13 times for the test set. |
| Hardware Specification | Yes | This work has been partially supported by a VIDI grant (639.072.513) from the Netherlands Organization for Scientific Research and a GPU grant (Ge Force Titan X) from the Nvidia Corporation. |
| Software Dependencies | No | The paper mentions several software components like 'Chainer and Cupy with CUDA and cu DNN', 'Caffe', 'scikit-learn', 'SPM', 'FSL', and 'Adam', but it does not provide specific version numbers for any of these components. |
| Experiment Setup | Yes | Model parameters were initialized as follows: biases were set to zero, the scaling parameters were drawn from N(1, 2 · 10−2I), the shifting parameters were set to zero and the weights were drawn from N(1, 10−2I) [37]. We set the hyperparameters of the loss functions as follows: λadv = 102, λdis = 102, λfea = 10−2 and λsti = 2 · 10−6 [38]. We set the hyperparameters of the optimizer as follows: α = 0.001, β1 = 0.9, β2 = 0.999 and ϵ = 108 [37]. |