Latent Space Factorisation and Manipulation via Matrix Subspace Projection
Authors: Xiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang, Frank Guerin
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods. The quality of generation of our new model (e.g. reconstruction, conditional generation) is highly competitive to a number of strong baselines. |
| Researcher Affiliation | Academia | 1Department of Computing Science, University of Aberdeen, UK 2Department of Computer Science, University of Sheffield, UK 3Department of Computer Science, University of Surrey, UK. Correspondence to: Chenghua Lin <c.lin@sheffield.ac.uk>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for our model is available online1. 1Code: https://xiao.ac/proj/msp |
| Open Datasets | Yes | We evaluated on the Celeb A dataset (Liu et al., 2015) (202,600 images) and trained one model on all 40 labelled attributes. and In this task, we adopt the E2E corpus (Duˇsek et al., 2019), which contains 50k+ reviews of restaurants... |
| Dataset Splits | No | The paper states the use of Celeb A and E2E corpus datasets but does not explicitly provide training, validation, and test dataset splits with specific percentages or counts. |
| Hardware Specification | Yes | The model is trained for 1000 epochs (on a Tesla T4 around 12 hours). |
| Software Dependencies | No | The paper mentions optimizers and other models but does not provide specific version numbers for software dependencies or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We used the ADAM optimiser with learning rate = 0.0002, mini-batch size of 256, and images are upsampled to 256 256. and The model is trained for 1000 epochs (on a Tesla T4 around 12 hours). |