Recursive Disentanglement Network
Authors: Yixuan Chen, Yubin Shi, Dongsheng Li, Yujiang Wang, Mingzhi Dong, Yingying Zhao, Robert P. Dick, Qin Lv, Fan Yang, Li Shang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies demonstrate that Recur D outperforms β-VAE and several of its state-of-the-art variants on disentangled representation learning and enables more data-efficient downstream machine learning tasks. |
| Researcher Affiliation | Collaboration | Yixuan Chen , Yubin Shi , Dongsheng Li {yixuanchen20, ybshi21}@fudan.edu.cn, dongsli@microsoft.com Yujiang Wang , Mingzhi Dong yujiang.wang14@imperial.ac.uk, mingzhidong@gmail.com Yingying Zhao , Robert Dick yingyingzhao@fudan.edu.cn, dickrp@umich.edu Qin Lv , Fan Yang , Li Shang qin.lv@colorado.edu, {yangfan, lishang}@fudan.edu.cn * China and Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China, Microsoft Research Asia, Shanghai, China, Department of Computing, Imperial College London, London, United Kingdom, Department of Electrical Engineering and Computer Science, University of Michigan, Michigan, United States, Department of Computer Science, University of Colorado Boulder, Boulder, United States, || School of Microelectronics, Fudan University, Shanghai, China, ** The corresponding author. |
| Pseudocode | No | The paper describes the model and learning process in natural language and diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code, nor does it provide links to a code repository. |
| Open Datasets | Yes | Datasets: We consider two datasets in which each image is obtained by a deterministic function of ground-truth factors: d Sprites (Matthey et al., 2017) and 3DShapes (Burgess & Kim, 2018). |
| Dataset Splits | No | The paper states 'For all experiments, we use a 9:1 training to testing data ratio', but does not explicitly provide details for a validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or library versions). |
| Experiment Setup | Yes | During training, we use Adam optimiser with learning rate 1e 4, β1 = 0.9, β2 = 0.999 for parameter updates. Specially, we utilize Recur D 1 on d Sprites, 3DCars and 3DShapes, and Recur D 2 on Celeb A. |