Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
Authors: Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos, Volkan Cevher
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the interpolation abilities of DDPMs trained on examples with one factor of interest in Section 5. We demonstrate this ability on real-world datasets, filtered to retain examples with clear attributes only, as depicted in Figure 1 (right). Importantly, the training samples are highly separated in our experiments. |
| Researcher Affiliation | Academia | 1Department of Computer Science, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland 2LIONS, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland 3Department of Electrical and Computer Engineering, University of Wisconsin-Madison, USA. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available on Git Hub. |
| Open Datasets | Yes | Real-world datasets like Celeb A, despite their discrete labels, contain some diversity in attributes. For instance, the Celeb A dataset contains clearly as well as mildly smiling faces, in the sense of Section 4.1. We train Efficient Net classifiers (Tan & Le, 2021), following Okawa et al. (2023). |
| Dataset Splits | No | The paper describes the process for filtering the Celeb A dataset to create extreme examples for training the DDPMs, and mentions a 'validation set' for auxiliary classifiers (Appendix D.2) and a 'held-out' manually labeled set (Appendix D.1), but it does not provide explicit train/test/validation splits for the main DDPM training data itself. |
| Hardware Specification | Yes | Training for 250k steps ranged from 19 to 21 hours on A100 40Gi B or RTX4090 respectively. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and details training parameters, but it does not provide specific version numbers for software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We train the diffusion model for 250k steps with learned denoising process variance, a learning rate of 1e 4, no weight decay, an EMA rate of 0.9999, 4000 diffusion steps, and the cosine noise schedule presented in Equation (10), well-suited for 64 64 images. |