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..
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
Authors: Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos, Volkan Cevher
ICML 2024 | Venue PDF | 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. |