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..
Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
Authors: Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present convincing results in a few visual domains, such as no-glasses to glasses, adding facial hair based on a reference image, etc. |
| Researcher Affiliation | Collaboration | Ori Press, Tomer Galanti & Sagie Benaim The School of Computer Science Tel Aviv University [...] Lior Wolf Facebook AI Research & The School of Computer Science Tel Aviv University |
| Pseudocode | No | The paper describes the architecture and training losses but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described method is publicly available. |
| Open Datasets | Yes | Images from the celeb A face image dataset by Yang et al. (2015) were used, since these are conveniently annotated as having the attribute or not. |
| Dataset Splits | No | The paper does not explicitly specify validation dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like Instance Normalization, Batch Normalization, leaky-ReLUs, and ReLUs, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Let Ck denote a Convolution Instance Norm-Re LU layer with k filters, where a kernel size of 4 4, with a stride of 2, and a padding of 1 is used. The activations of the encoders e1, e2 are leaky-Re LUs with a slope of 0.2 and the deocder g employs Re LUs. e1 has the following layers C32, C64, C128, C256, C512, C512 d; e2 has a slightly lower capacity C32, C64, C128, C128, C128, Cd, where d = 25. The input images have a size of 128 128, and the encoding is of size 512 2 2 (split between the e1 and e2). g is symmetric to the encoders and employs transposed convolutions for the upsampling. [...] Our method has one weighting hyperparameter, which is fixed throughout the experiments. |