Invertible Convolutional Flow
Authors: Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We first conduct experiments to evaluate the benefits of the proposed flow model (CONF). We compare the density estimation performance of CONF to the affine coupling flow models real NVP [Dinh et al., 2016] and Glow [Kingma and Dhariwal, 2018], and the recent continuous-time invertible generative model FFJORD [Grathwohl et al., 2019]. |
| Researcher Affiliation | Collaboration | Mahdi Karami Department of Computer Science University of Alberta karami1@ualberta.ca Jascha Sohl-Dickstein Dale Schuurmans Laurent Dinh Daniel Duckworth Google Brain |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We also perform unconditional density estimation on two image datasets; MNIST, consisting of handwritten digits [Y. Le Cun, 1998] and CIFAR-10, consisting of natural images [Krizhevsky, 2009]. |
| Dataset Splits | No | Details of model architecture and experimental setup together with more empirical results are presented in appendix. The paper refers to standard datasets and implicitly uses test sets (Table 1), but explicit train/validation/test splits (e.g., percentages or counts) are not provided in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Each coupling flow is composed of a maximum of M = 2 iterates of the combined convolution flow. the parameters of the nonlinear bijector pair, {σα, σα }, are initialized sufficiently close to zero so that they behave approximately as linear functions at the outset. Furthermore, the conditioning networks are initialized such that the scaling filters, s, and the convolution kernels at the frequency domain, F{w}, are all initially identity filters. |