Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation
Authors: Xin Yuan, Michael Maire
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that our model achieves accurate unsupervised image segmentation and high-quality synthetic image generation across multiple datasets. |
| Researcher Affiliation | Collaboration | Google yuanxzzz@google.com Michael Maire University of Chicago mmaire@uchicago.edu |
| Pseudocode | Yes | Algorithm 1 Training Masked Diffusion ... Algorithm 2 Image and Mask Generation |
| Open Source Code | No | Answer: [No] Justification: We will release the code upon acceptance. |
| Open Datasets | Yes | We evaluate on: (1) real image segmentation, (2) image and region mask generation, using Flower [45], CUB [62], FFHQ [31], Celeb AMask-HQ [36], and Image Net [53]. |
| Dataset Splits | Yes | For unsupervised segmentation on Flower and CUB, we follow the data splitting in IEM [54] and evaluate predicted mask quality under three commonly used metrics, denoted as Acc., IOU and DICE score [54, 9]. |
| Hardware Specification | Yes | For all datasets except Image Net, we train 64 64 and 128 128 models on 8 and 32 Nvidia V100 32GB GPUs, respectively. |
| Software Dependencies | No | The paper mentions the use of U-Net and Adam optimizer but does not specify software dependencies like Python, PyTorch/TensorFlow, or CUDA versions. |
| Experiment Setup | Yes | We use Adam to train all the models with a learning rate of 10 4 and an exponential moving average (EMA) over model parameters with rate 0.9999. ... For Flower, CUB and FFHQ, we train the models for 50K, 50K, 500K iterations with batch size of 128, respectively. For Image Net, we train 500K iterations on 32 Nvidia V100 GPUs with batch size 512. We adopt the linear noise scheduler as in Ho et al. [27] with T = 1000 timesteps. |