On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks
Authors: Anish Chakrabarty, Swagatam Das
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this study, we investigate the statistical properties of such unpaired data translator networks between distinct spaces, bearing the additional responsibility of cycle-consistency. In a density estimation setup, we derive sharp non-asymptotic bounds on the translation errors under suitably characterized models. This, in turn, points out sufficient regularity conditions that maps must obey to carry out successful translations. We further show that cycle-consistency is achieved as a consequence of the data being successfully generated in each space based on observations from the other. In a first-of-its-kind attempt, we also provide deterministic bounds on the cumulative reconstruction error. |
| Researcher Affiliation | Academia | Anish Chakrabarty Statistics and Mathematics Unit Indian Statistical Institute, Kolkata West Bengal, India Swagatam Das Electronics and Communication Sciences Unit Indian Statistical Institute, Kolkata West Bengal, India |
| Pseudocode | No | The paper contains mathematical formulations, definitions, theorems, lemmas, and proofs, but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any open-source code for the described methodology. The self-assessment explicitly states N/A for code availability. |
| Open Datasets | No | The paper is theoretical and discusses data distributions as theoretical constructs. It does not mention using any specific public or open dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore it does not provide any training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. The authors' self-assessment also states N/A for compute resources. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. The authors' self-assessment also states N/A for training details. |