Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
Authors: Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | 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. |