Dual Swap Disentangling
Authors: Zunlei Feng, Xinchao Wang, Chenglong Ke, An-Xiang Zeng, Dacheng Tao, Mingli Song
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image datasets from a wide domain show that our model yields state-of-the-art disentangling performances. |
| Researcher Affiliation | Collaboration | Zunlei Feng Zhejiang University zunleifeng@zju.edu.cnXinchao Wang Stevens Institute of Technology xinchao.wang@stevens.eduChenglong Ke Zhejiang University chenglongke@zju.edu.cnAnxiang Zeng Alibaba Group renzhong@taobao.comDacheng Tao University of Sydney dctao@sydney.edu.auMingli Song Zhejiang University brooksong@zju.edu.cn |
| Pseudocode | Yes | Algorithm 1 The Dual Swap Disentangling (DSD) algorithm |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code, nor does it provide a specific repository link for the methodology described in the paper. |
| Open Datasets | Yes | We conduct experiments on six image datasets of different domains: a synthesized Square dataset, Teapot (Moreno et al. [2016], Eastwood and Williams [2018]), MNIST (Haykin and Kosko [2009]), d Sprites (Higgins et al. [2016]), Mugshot (Shen et al. [2016]), and CAS-PEAL-R1 (Gao et al. [2008]). |
| Dataset Splits | Yes | Square: The training, validation and testing dataset are set as {(20, 000), (9, 000) and (1, 000)}, respectively.Teapot: In the experiment, we used 50, 000 training, 10, 000 validation and 10, 000 testing samples.d Sprites: We sample 100, 000 pairs from original d Sprites, which are divided into {(80, 000), (10, 000), (10, 000)} for training, validation and testing.Mugshot: For Mugshot dataset, we divided it into {(20, 000), (9, 000), (1, 000)} for training, validation and testing.CAS-PEAL-R1: They are divided into {(40, 000), (9, 000), (1, 000)} for training, validation and testing. |
| Hardware Specification | No | The paper mentions network architectures and optimizers but does not provide specific hardware details such as GPU/CPU models or memory used for experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer', 'Info GAN', 'Wasserstein GAN', and 'layer normalization' but does not provide specific version numbers for them. |
| Experiment Setup | Yes | Adam optimizer (Kingma and Ba [2014]) is adopted with learning rates of 1e 4 (64 64 network) and 0.5e 4 (32 32 network). The batch size is 64. For the above two network architecture, α and β are all set as 5 and 0.2, respectively. The encoder / discriminatior (D) / auxilary network (Q) and the decoder / generator (G) are shown in Table 1. |