Conditional Image Generation by Conditioning Variational Auto-Encoders
Authors: William Harvey, Saeid Naderiparizi, Frank Wood
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach on several conditional generation tasks in the image domain but focus in particular on stochastic image completion: the problem of inferring the posterior distribution over images given the observation of a subset of pixel values. ... This is supported empirically by results indicating that, not only is the visual quality of our image completions (see Fig. 1) close to the state-of-the-art (Zhao et al., 2021), but our coverage of the true posterior over image completions is superior to that of any of our baselines. 4 EXPERIMENTS |
| Researcher Affiliation | Collaboration | William Harvey, Saeid Naderiparizi & Frank Wood Department of Computer Science University of British Columbia Vancouver, Canada {wsgh,saeidnp,fwood}@cs.ubc.ca Frank Wood is also affiliated with the Montr eal Institute for Learning Algorithms (Mila) and Inverted AI. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release code for training IPA and IPA-R, code for using the trained artifacts to perform Bayesian experimental design and out-of-distribution detection, and various pretrained models1. 1https://github.com/plai-group/ipa |
| Open Datasets | Yes | We create an IPA image completion model based on the VD-VAE unconditional architecture (Child, 2020), and evaluate it for image completion on three datasets: CIFAR-10 (Krizhevsky et al., 2009), Image Net-64 (Deng et al., 2009), and FFHQ256 (Karras et al., 2019). And We experiment on the NIH Chest X-ray 14 dataset (Wang et al., 2017) at 256 256 resolution. |
| Dataset Splits | No | The paper references standard datasets like CIFAR-10, ImageNet-64, FFHQ-256, and NIH Chest X-ray 14, and mentions using 'test sets' from these. However, it does not provide specific details on the train/validation/test splits (e.g., percentages or sample counts for each split) used for its experiments. |
| Hardware Specification | Yes | CIFAR-10: GPUs V100, 2080 Ti... Image Net-64: GPUs V100, 2080 Ti... FFHQ-256: GPUs V100, 2080 Ti... Edges2Bags: GPUs 2080 Ti... Edges2Shoes: GPUs 2080 Ti... Chest X-ray: GPUs 2080 Ti, V100 and We trained the Image Net-32 VAE on a Ge Force RTX 2080 Ti for 14 days... We trained the Chest X-ray VAE on 4 V100 GPUs for about 5 days |
| Software Dependencies | No | The paper mentions 'Weights & Biases (Biewald, 2020)' as an experiment-tracking infrastructure but does not provide specific version numbers for any software dependencies, such as deep learning frameworks or libraries. |
| Experiment Setup | Yes | Most training hyperparameters were the same as those used by Child (2020) for unconditional training of the corresponding architectures. We report the significant differences in Table 3 and the following paragraph. Learning rates were selected with sweeps over three values, and the batch sizes selected were the largest compatible with the GPU s memory. And We train g to estimate these using a cross-entropy loss. ... for 32 000 iterations with a batch size of 32 and learning rate 1 10 5. |