Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Conditional Image Generation by Conditioning Variational Auto-Encoders
Authors: William Harvey, Saeid Naderiparizi, Frank Wood
ICLR 2022 | Venue PDF | 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 EMAIL 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. |