Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Authors: Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, Tian Tian
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For image density modeling on the CIFAR-10 dataset, VFlow achieves a new state-of-the-art 2.98 bits per dimension. We first evaluate VFlow on a toy DX = 2 Checkerboard dataset... We study the impact of the dimensionality of the flow DX + DZ {2, 4, 6, 8, 10}... We evaluate VFlow on CIFAR-10 and Image Net for density estimation of images. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Technology, Institute for AI, BNRist Center, Tsinghua University 2Real AI. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is open-sourced at https://github.com/thu-ml/vflow. |
| Open Datasets | Yes | For image density modeling on the CIFAR-10 dataset... We evaluate VFlow on CIFAR-10 and Image Net (Russakovsky et al., 2015) for density estimation of images. |
| Dataset Splits | Yes | For this set of experiments, we randomly hold out 10,000 samples from the training set for validation. |
| Hardware Specification | Yes | All the experiments are run on 16 RTX 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions using an "Adam optimizer (Kingma & Ba, 2015)" but does not provide specific version numbers for software libraries, frameworks, or programming languages used (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | The model is trained with an Adam optimizer (Kingma & Ba, 2015) with a batch size 64 for 2,000 epochs. Following (Ho et al., 2019), the learning rate linearly warms up to 0.0012 during the first 2,000 training steps, and exponentially decays at a rate of 0.99999 per step starting from the 50,000-th step until it reaches 0.0003. |