Glow: Generative Flow with Invertible 1x1 Convolutions
Authors: Durk P. Kingma, Prafulla Dhariwal
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we propose Glow, a simple type of generative flow using an invertible 1 1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a flow-based generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. ... 5 Quantitative Experiments ... 6 Qualitative Experiments |
| Researcher Affiliation | Collaboration | Diederik P. Kingma* , Prafulla Dhariwal *Open AI Google AI |
| Pseudocode | No | The paper describes its proposed generative flow and its components in detail using text and mathematical equations, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for our model is available at https://github.com/openai/glow. |
| Open Datasets | Yes | We compare the average negative log-likelihood (bits per dimension) on the CIFAR-10 (Krizhevsky, 2009) dataset... In particular, we compare on CIFAR-10, Image Net (Russakovsky et al., 2015) and LSUN (Yu et al., 2015) datasets. ... We choose the Celeb A-HQ dataset (Karras et al., 2017) |
| Dataset Splits | Yes | Since the original Celeb A HQ dataset didn’t have a validation set, we separated it into a training set of 27000 images and a validation set of 3000 images. |
| Hardware Specification | Yes | More specifically, generating a 256 256 image at batch size 1 takes about 130ms on a single NVIDIA GTX 1080 Ti, and about 550ms on a NVIDIA Tesla K80. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components such as programming languages, libraries (e.g., PyTorch), or frameworks used for the experiments. |
| Experiment Setup | Yes | In our experiments, we let each NN() have three convolutional layers, where the two hidden layers have Re LU activation functions and 512 channels. The first and last convolutions are 3 3, while the center convolution is 1 1... All models were trained with K = 32 and L = 3. ... K = 32 and L = 6. ... minibatch size 1 per PU, and use gradient checkpointing (Salimans and Bulatov, 2017). |