Isometric Representation Learning for Disentangled Latent Space of Diffusion Models
Authors: Jaehoon Hahm, Junho Lee, Sunghyun Kim, Joonseok Lee
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments illustrate advantages of the proposed method in image interpolation, image inversion, and linear editing. and We conduct extensive experiments to verify the effectiveness of our isometric loss Liso on disentangling the latent space of diffusion models. |
| Researcher Affiliation | Collaboration | 1Seoul National University, Seoul, Korea 2Google Research, Mountain View, California, United States. |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our source code is publicly available at https://github.com/isno0907/isodiff. Readers would be able to reproduce the reported results by running this code. |
| Open Datasets | Yes | Dataset. We evaluate our approach on CIFAR-10, Celeb AHQ (Huang et al., 2018), LSUN-Church and LSUNBedrooms (Wang et al., 2017). |
| Dataset Splits | No | The paper mentions training partition sizes for each dataset, but does not explicitly provide details about train/validation/test splits (percentages or counts) that would allow reproduction of the data partitioning. |
| Hardware Specification | Yes | We use 4 NVIDIA A100 GPUs with 40GB memory for experiments. |
| Software Dependencies | No | The paper mentions the use of an Adam optimizer and U-Net architecture, but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | If not specified, we train with batch size 32, learning rate 10 4, p = 0.5, and λiso = 10 4 for 10 epochs by default. ... We use Adam optimizer and exponential moving average (Brown, 1956) on model parameters with a decay factor of 0.9999. |