Hyperprior Induced Unsupervised Disentanglement of Latent Representations
Authors: Abdul Fatir Ansari, Harold Soh3175-3182
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on a range of datasets (2DShapes, 3DChairs, 3DFaces and Celeb A) show our approach to outperform the β-VAE and is competitive with the state-of-the-art Factor VAE. Our approach achieves significantly better disentanglement and reconstruction on a new dataset (Correlated Ellipses) which introduces correlations between the factors of variation. |
| Researcher Affiliation | Academia | Abdul Fatir Ansari, Harold Soh Department of Computer Science, National University of Singapore {afatir, harold}@comp.nus.edu.sg |
| Pseudocode | No | The paper does not contain a pseudocode block or an explicitly labeled algorithm section. |
| Open Source Code | Yes | details are available in the supplementary material and our code base is available for download at https://github.com/crslab/CHy VAE. |
| Open Datasets | Yes | 2DShapes (or d Sprites) (Matthey et al. 2017): 737,280 binary 64 64 images of 2D shapes... 3DFaces (Paysan et al. 2009): 239,840 greyscale 64 64 images of 3D Faces. 3DChairs (Aubry et al. 2014): 86,366 RGB 64 64 images of CAD chair models. Celeb A (Liu et al. 2015): 202,599 RGB images of celebrity faces center-cropped to dimensions 64 64. |
| Dataset Splits | No | The paper does not provide specific details regarding dataset splits for training, validation, or testing, nor does it cite a standard split used for these datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' and a 'convolutional neural network (CNN) for the encoder and a deconvolutional NN for the decoder' but does not specify software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | To ease comparisons between the methods and prior work, we use the same network architecture across all the compared methods. Specifically, we follow the model in (Kim and Mnih 2018): a convolutional neural network (CNN) for the encoder and a deconvolutional NN for the decoder. We normalize all datasets to [0, 1] and use sigmoid cross-entropy as the reconstruction loss function. For training, we use Adam optimizer (Kingma and Ba 2014) with a learning rate of 10 4. For the discriminator in Factor VAE, we use the parameters recommended by Kim and Mnih (2018). |