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 [1].
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Authors: Xudong Pan, Mi Zhang, Daizong Ding
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we reformulate their model from a brand-new geometrical perspective and have eventually reached a full interpretation on some interesting but unclear empirical phenomenons from their experiments. Furthermore, by extending the de๏ฌnition of generalization for generative adversarial nets (Arora et al., 2017) to a broader sense, we have derived a condition to control the generalization capability of their model. |
| Researcher Affiliation | Academia | 1Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, China. Correspondence to: Mi Zhang <mi EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. It focuses on theoretical formulations and proofs. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and analyzes existing models, it does not describe training a new model with a specific dataset or provide access information for a dataset used for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers, as it does not present new experimental implementations. |
| Experiment Setup | No | The paper is theoretical and does not conduct new experiments. Therefore, it does not provide specific experimental setup details such as hyperparameters or system-level training settings. |