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..
Adaptive Density Estimation for Generative Models
Authors: Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our approach on the CIFAR-10 dataset with an ablation study. Our model significantly improves over existing hybrid models, producing GAN-like samples, and IS and FID scores that are competitive with fully adversarial models, see Figure 3. At the same time, we obtain likelihoods on held-out data comparable to state-of-the-art likelihood-based methods which requires covering the full support of the dataset. We further confirm these observations with quantitative and qualitative experimental results on the STL-10, Image Net and LSUN datasets. |
| Researcher Affiliation | Collaboration | Thomas Lucas Inria Konstantin Shmelkov , Noah s Ark Lab, Huawei Cordelia Schmid Inria Karteek Alahari Inria Jakob Verbeek Inria |
| Pseudocode | Yes | The training procedure, written as an algorithm in Appendix H, alternates between (i) bringing LQ(pθ,ψ) closer to it s optimal value L Q(pθ,ψ) = DKL(pθ,ψ||p ), and (ii) minimizing LC(pθ,ψ)+LQ(pθ,ψ). |
| Open Source Code | No | The paper does not provide a direct link to open-source code or explicitly state that the code for the methodology is available. |
| Open Datasets | Yes | We experimentally validate our approach on the CIFAR-10 dataset with an ablation study... We further confirm these observations with quantitative and qualitative experimental results on the STL-10, Image Net and LSUN datasets. |
| Dataset Splits | No | We use the standard split of 50k/10k train/test images of 32 32 pixels. |
| Hardware Specification | Yes | Figure 7 shows samples from our generator trained on a single GPU with 11 Gb of memory on LSUN classes. |
| Software Dependencies | No | The paper mentions optimizers like Adam but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | No | The paper describes model architectures and training objectives but does not provide specific hyperparameter values like learning rate, batch size, or number of epochs in the main text. |