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..
Explicitly Minimizing the Blur Error of Variational Autoencoders
Authors: Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil, Ender Konukoglu
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the potential of the proposed loss on three different data sets, where it outperforms several recently proposed reconstruction losses for VAEs. |
| Researcher Affiliation | Academia | Gustav Bredell, Kyriakos Flouris, Krishna Chaitanya, Ertunc Erdil & Ender Konukoglu Department of Information Technology and Electrical Engineering ETH Zurich EMAIL |
| Pseudocode | No | The paper describes the approach using mathematical formulations and textual descriptions but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | Details on the architecture can be found in the code that will be publicly available upon acceptance. |
| Open Datasets | Yes | To evaluate the potential of the proposed approach on natural images we make use of the popular Celeb A dataset as provided by Liu et al. (2015) and Lee et al. (2020) for low and high-resolution images, respectively. |
| Dataset Splits | No | For all the datasets we use 80% for the training set and 20% for the test set. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., CPU, GPU models, or memory size) used for running the experiments. |
| Software Dependencies | No | The code is written in Python and Py Torch (Paszke et al. (2019)) is used as library for the deep learning models. |
| Experiment Setup | Yes | Furthermore, the Adam optimizer is used with a learning rate of 1e 4. For the lowand high-resolution Celeb A the number of training epochs were, 100 and 200, respectively. For the HCP dataset the models were trained for 400 epochs. |