On Incorporating Inductive Biases into VAEs
Authors: Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show their superiority compared with baseline methods in both generation and feature quality, most notably providing state-of-the-art performance for learning sparse representations in the VAE framework. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Oxford, 2University of Edinburgh |
| Pseudocode | No | The paper describes the computational steps and formulas, but does not contain a structured pseudocode or algorithm block that is clearly labeled as such. |
| Open Source Code | Yes | Accompanying code is provided at https://github.com/Ning Miao/Inte L-VAE. |
| Open Datasets | Yes | For real datasets, We load MNIST, Fashion-MNIST, and Celeb A directly from Tensorflow (Abadi et al., 2015) |
| Dataset Splits | Yes | Dataset sizes Unlimited 55k/5k/10k 55k/5k/10k 10k/1k/2k 163k/20k/20k Input space R2 Binary 28x28 Binary 28x28 Binary 28x28 RGB 64x64x3 |
| Hardware Specification | Yes | All experiments are run on a GTX-1080-Ti GPU. |
| Software Dependencies | No | The paper mentions using 'Tensorflow' but does not provide specific version numbers for it or any other key software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Table C.1: Hyperparameters used for different experiments. This table specifies 'Batch size', 'Optimizer Adam', and 'Learning rate'. |