Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
Authors: Dazhong Shen, Chuan Qin, Chao Wang, Hengshu Zhu, Enhong Chen, Hui Xiong
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments on three benchmark datasets clearly show that our approach can outperform state-of-the-art baselines on both likelihood estimation and underlying classification tasks. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science and Technology, University of Science and Technology of China 2Baidu Talent Intelligence Center 3Rutgers, The State University of New Jersey |
| Pseudocode | Yes | Algorithm 1 Training Procedure of DU-VAE |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code. |
| Open Datasets | Yes | we evaluated our method on two text benchmark datasets, i.e., Yahoo and Yelp corpora [Yang et al., 2017] and one image benchmark dataset, i.e., OMNIGLOT [Lake et al., 2015]. |
| Dataset Splits | No | The paper mentions using 'dynamically binarized images for training and the fixed binarization as test data' for OMNIGLOT, but does not specify a validation split or provide specific numeric splits for all datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adam) and model architectures (LSTM, ResNet, Pixel CNN) but does not provide specific software names with version numbers for reproducibility (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | For text datasets, we utilized a single layer LSTM as both encoder and decoder networks... For images, a 3-layer Res Net [He et al., 2016] encoder and a 13-layer Gated Pixel CNN [Van den Oord et al., 2016] decoder are applied. We set the dimension of z as 32. and utilized SGD to optimize the ELBO objective for text and Adam [Kingma and Ba, 2015] for images. Following [Bowman et al., 2015a], we applied a linear annealing strategy to increasing the KL weight from 0 to 1 in the first 10 epochs if possible. |