Why do Variational Autoencoders Really Promote Disentanglement?
Authors: Pratik Bhowal, Achint Soni, Sirisha Rambhatla
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Complementary to our theoretical contributions, our experimental results corroborate our analysis. Code is available at https://github.com/criticalml-uw/ Disentanglement-in-VAE.In this section, we discuss the experimental setup and the results to verify our theoretical findings. We experimentally verify how introducing local non-linearity makes the VAE modeling more realistic. |
| Researcher Affiliation | Collaboration | 1NVIDIA, India 2Department of Computer Science, University of Waterloo, Ontario, Canada 3Department of Management Science and Engineering, University of Waterloo, Ontario, Canada. |
| Pseudocode | No | The paper contains mathematical proofs and lemmas, but no clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/criticalml-uw/ Disentanglement-in-VAE. |
| Open Datasets | Yes | We study the VAE architectures using four widely used datasets, namely, d Sprites, 3D Faces (Paysan et al., 2009), 3D shapes (Burgess & Kim, 2018), and MPI 3D complex real-world shapes dataset (Gondal et al., 2019). |
| Dataset Splits | No | A validation set x(i) Xval is defined. For each x(i), g D and MD are estimated as neural networks, considering that these are local approximations unique to each x(i). (11) is employed to train these networks, as detailed in Sect. 4.4. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | Table 3 mentions 'Adam' as an optimizer with a learning rate, but no specific software or library versions (e.g., Python, PyTorch, TensorFlow, scikit-learn) are provided to ensure reproducibility. |
| Experiment Setup | Yes | Table 3. The architectures of the VAE-based models used for the different datasets. Dataset Component Architecture... Encoder Conv [32, 4, 2, 1] (Re LU), [32, 4, 2, 1] (Re LU), [64, 4, 2, 1] (Re LU), [64, 4, 2, 1] (BN) (Re LU), [256, 4, 1, 0] (BN) (Re LU), [Latent Space, 1, 1] (Re LU) Decoder Conv[64, 1, 1, 0] (Relu), Conv Trans [64, 4, 1, 0] (Re LU), [64, 4, 2, 1] (Re LU), [64, 4, 2, 1] (Re LU), [32, 4, 2, 1] (Re LU), [32, 4, 2, 1] (Re LU), [3, 4, 2, 1] β 6 Optimizer Adam (lr = 10 3, betas = (0.9, 0.999)) |