Multimodal Adversarially Learned Inference with Factorized Discriminators
Authors: Wenxue Chen, Jianke Zhu6304-6312
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted experiments on the benchmark datasets, whose promising results show that our proposed approach outperforms the-state-of-the-art methods on a variety of metrics. |
| Researcher Affiliation | Collaboration | 1 Zhejiang University 2 Alibaba-Zhejiang University Joint Institute of Frontier Technologies {wxchern, jkzhu}@zju.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is publicly available at https://github.com/6b5d/mmali. |
| Open Datasets | Yes | We have conducted experiments on the benchmark datasets...Multi MNIST dataset...MNIST-SVHN dataset...Caltech-UCSD Birds (CUB) dataset (Welinder et al. 2010) |
| Dataset Splits | No | The paper references benchmark datasets (Multi MNIST, MNIST-SVHN, CUB) but does not explicitly provide the specific percentages or sample counts for training, validation, and test splits, nor does it specify which standard splits (if any) are used for these datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Across all the experiments, we use Adam optimizer (Kingma and Ba 2015) with the learning rate 0.0002, in which all models are trained for 250, 000 iterations with the batch size 64. We also employ an exponential moving average (Yazici et al. 2019) of the weights for both encoders and decoders with a decay rate 0.9999 and start at the 50, 000-th iteration. In all models, the standard Gaussian N(0, I) is chosen as the prior, and the isotropic Gaussian N(µ, σ2I) is chosen as the posterior. We make use of the non-saturating loss described in Dandi et al. (2021) to update the encoders and decoders. |