Deep Automodulators
Authors: Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | demonstrated on four image data sets. Besides style-mixing, we show state-of-the-art results in autoencoder comparison, and visual image quality nearly indistinguishable from state-of-the-art GANs. We demonstrate promising qualitative and quantitative performance and applications on FFHQ, CELEBA-HQ, and LSUN Bedrooms and Cars data sets. |
| Researcher Affiliation | Collaboration | Ari Heljakka1,2 Yuxin Hou1 Juho Kannala1 Arno Solin1 1Aalto University 2Gen Mind {ari.heljakka, yuxin.hou, juho.kannala, arno.solin}@aalto.fi |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our source code is available at https://github.com/AaltoVision/automodulator. |
| Open Datasets | Yes | We demonstrate promising qualitative and quantitative performance and applications on FFHQ, CELEBA-HQ, and LSUN Bedrooms and Cars data sets. |
| Dataset Splits | No | The paper mentions training on 'the actual 60k training set of FFHQ only' and 'CELEBA-HQ at 256x256 with 40M seen samples (50k FID batch)', but does not provide specific percentages or counts for training, validation, and test splits across all datasets, nor does it explicitly reference predefined standard splits. |
| Hardware Specification | No | The paper only states that 'The authors wish to acknowledge the Aalto Science-IT project and CSC IT Center for Science, Finland, for computational resources,' without providing specific hardware details like GPU or CPU models. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers, such as Python, PyTorch, or TensorFlow versions. |
| Experiment Setup | Yes | Margin Mgap = 0.5, except for CELEBAHQ and Bedrooms 128 128 (Mgap = 0.2) and CELEBAHQ 256 256 (Mgap = 0.4). We train the model variants on CELEBA-HQ [24] data set to convergence (40M seen samples) and choose the best of three restarts with different random seeds. |