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..
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 | Venue PDF | 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 EMAIL Byeonghu Na KAIST EMAIL 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). |