Exact Rate-Distortion in Autoencoders via Echo Noise
Authors: Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We proceed to analyse the log-likelihood performance of relevant models on three image datasets: static Binary MNIST [38], Omniglot [24] as adapted by Burda et al. [8], and Fashion MNIST (f MNIST) [44]. All models are trained with 32 latent variables using the same convolutional architecture as in Alemi et al. [3] except with Re LU activations. We trained using Adam optimization for 200 epochs, with an initial learning rate of 0.0003 decaying linearly to 0 over the last 100 epochs. Results are averaged from ten runs of each model after removing the highest and lowest outliers. |
| Researcher Affiliation | Academia | Information Sciences Institute University of Southern California Marina del Rey, CA 90292 brekelma, moyerd@usc.edu; galstyan, gregv@isi.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology it describes. |
| Open Datasets | Yes | We proceed to analyse the log-likelihood performance of relevant models on three image datasets: static Binary MNIST [38], Omniglot [24] as adapted by Burda et al. [8], and Fashion MNIST (f MNIST) [44]. |
| Dataset Splits | No | The paper mentions training and testing but does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts) needed for reproduction. While it evaluates on 'test' sets, it does not specify how the data was partitioned for validation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimization' and 'Re LU activations' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | All models are trained with 32 latent variables using the same convolutional architecture as in Alemi et al. [3] except with Re LU activations. We trained using Adam optimization for 200 epochs, with an initial learning rate of 0.0003 decaying linearly to 0 over the last 100 epochs. |