Maximum Likelihood Training of Implicit Nonlinear Diffusion Model
Authors: Dongjun Kim, Byeonghu Na, Se Jung Kwon, Dongsoo Lee, Wanmo Kang, Il-chul Moon
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, INDM achieves the state-of-the-art FID of 1.75 on Celeb A. We release our code at https://github.com/byeonghu-na/INDM. |
| Researcher Affiliation | Collaboration | Dongjun Kim KAIST dongjoun57@kaist.ac.kr Byeonghu Na KAIST wp03052@kaist.ac.kr Se Jung Kwon NAVER CLOVA Dongsoo Lee NAVER CLOVA Wanmo Kang KAIST Il-Chul Moon KAIST / Summary.AI |
| Pseudocode | Yes | Algorithm 1 Implicit Nonlinear Diffusion Model |
| Open Source Code | Yes | We release our code at https://github.com/byeonghu-na/INDM. |
| Open Datasets | Yes | This section quantitatively analyzes suggested INDM on CIFAR-10 and Celeb A 64 64. |
| Dataset Splits | No | We compute NLL/NELBO for performances of density estimation with Bits Per Dimension (BPD). |
| Hardware Specification | No | The paper acknowledges support from government grants but does not specify any particular hardware components such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | Throughout the experiments, we use NCSN++ with VESDE and DDPM++ with VPSDE [1] as the backbones of diffusion models, and a Res Net-based flow model [23, 24] as the backbone of the flow model. |
| Experiment Setup | Yes | See Appendix F for experimental details. We experiment with a pair of weighting functions for the score training. One is the likelihood weighting [11] with λ(t) = g2(t)... The other is the variance weighting [8] λ(t) = σ2(t)... We use either the Predictor-Corrector (PC) sampler [1] or a numerical ODE solver (RK45 [25])... For a better FID, we find the optimal signal-to-noise value (Table 14), sampling temperature (Table 15), and stopping time (Table 16). |