Simple and Effective VAE Training with Calibrated Decoders
Authors: Oleh Rybkin, Kostas Daniilidis, Sergey Levine
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training. Our analysis covers a range of image and video datasets and several single-image and sequential VAE models. |
| Researcher Affiliation | Academia | 1University of Pennsylvania 2UC Berkeley. Correspondence to: Oleh Rybkin <oleh@seas.upenn.edu>. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project website: https: //orybkin.github.io/sigma-vae/. |
| Open Datasets | Yes | We use a small convolutional VAE model on SVHN (Netzer et al., 2011), a larger hierarchical HVAE model (Maaløe et al., 2019) on the Celeb A (Liu et al., 2015) and CIFAR (Krizhevsky et al., 2009) datasets, and a sequence VAE model called SVG (Denton & Fergus, 2018) on the BAIR Pushing dataset (Finn & Levine, 2017). |
| Dataset Splits | No | The paper mentions the datasets used and image/frame sizes but does not provide specific train/validation/test splits (e.g., percentages, sample counts, or references to standard splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using software like TensorFlow and PyTorch by citing their papers, but it does not specify exact version numbers for any software dependencies. |
| Experiment Setup | Yes | We observe that setting λmin = 6 to lower bound the standard deviation to be at least half of the distance between allowed color values works well in practice. |