Adaptive Density Estimation for Generative Models
Authors: Thomas Lucas, Konstantin Shmelkov, Karteek Alahari, Cordelia Schmid, Jakob Verbeek
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |