Wavelet Score-Based Generative Modeling
Authors: Florentin Guth, Simon Coste, Valentin De Bortoli, Stephane Mallat
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we present numerical results on Gaussian distributions, the φ4 physical model at phase transition, and the Celeb A-HQ image dataset [19]. and Theorems controlling errors of time discretizations of SGMs, proving accelerations obtained by scale separation with wavelets. These results are empirically verified by showing that WSGM provides an acceleration for the synthesis of physical processes at phase transition and natural image datasets. |
| Researcher Affiliation | Collaboration | Florentin Guth Computer Science Department, ENS, CNRS, PSL University Simon Coste Computer Science Department, ENS, CNRS, PSL University Valentin De Bortoli Computer Science Department, ENS, CNRS, PSL University Stéphane Mallat Collège de France, Paris, France Flatiron Institute, New York, USA |
| Pseudocode | Yes | The generative process is illustrated in Figure 1 and its pseudocode is given in Algorithm 1 in Appendix S2. |
| Open Source Code | Yes | All the code needed to reproduce the experiments is provided in the supplemental material. |
| Open Datasets | Yes | We present numerical results on Gaussian distributions, the φ4 physical model at phase transition, and the Celeb A-HQ image dataset [19]. |
| Dataset Splits | No | The paper mentions training on Celeb A-HQ but does not explicitly state the training, validation, and test dataset splits within the main text. |
| Hardware Specification | Yes | We estimate the total amount of compute used during the preparation of this paper, including preliminary experiments, at around 10k hours of NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper mentions general software components like 'neural network' and 'U-Net architecture' but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Following [38], the global scores sθ(x) are parameterized by a neural network with a U-Net architecture. It has 3 residual blocks at each scale, and includes multi-head attention layers at lower scales. The conditional scores sθ( xj|xj) are parameterized in the same way, and the conditioning on the low frequencies xj is done with a simple input concatenation along channels [38, 40]. The details of the architecture are in Appendix S9. We use a uniform discretization of the backward SDE to stay in the setting of Theorem 2, and show that WSGM still obtains satisfactory results in this case. |