Hierarchical Uncertainty Exploration via Feedforward Posterior Trees
Authors: Elias Nehme, Rotem Mulayoff, Tomer Michaeli
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase our approach across diverse datasets and image restoration challenges, highlighting its prowess in uncertainty quantification and visualization. Our findings reveal that our method performs comparably to a baseline that hierarchically clusters samples from a diffusion-based posterior sampler, yet achieves this with orders of magnitude greater speed. |
| Researcher Affiliation | Academia | Elias Nehme Electrical and Computer Engineering Technion Israel Institute of Technology seliasne@campus.technion.ac.il Rotem Mulayoff CISPA Helmholtz Center for Information Security rotem.mulayof@gmail.com Tomer Michaeli Electrical and Computer Engineering Technion Israel Institute of Technology tomer.m@ee.technion.ac.il |
| Pseudocode | Yes | Algorithm 1 Tree Training |
| Open Source Code | Yes | Code and examples are available at our webpage. Our code is made publicly available on Git Hub. |
| Open Datasets | Yes | Handwritten Digits, Edges Shoes, and Human Faces. Figure 2 demonstrates posterior trees on inpainting the top 70% of handwritten digits from the MNIST dataset. ... We also applied posterior trees to the edges-to-shoes dataset taken from pix2pix [56, 57]. ... Next, we tested posterior trees on face images from the Celeb A-HQ dataset, using the split from Celeb A [58]. ... Here, we applied our method to a dataset of migrating cells, imaged in a spinning-disk microscope, simultaneously with two different fluorescent dyes (one staining the nuclei and one staining actin filaments) [59]. |
| Dataset Splits | No | For both components, the step size was reduced by a factor of 10 if the validation loss stagnated for more than 10 epochs, and the minimum step size was set to 5e-6. |
| Hardware Specification | Yes | Runtime is reported as both the speed of a forward pass (sec) and the memory usage (GB) required to infer a single test image with a batch of 1 on an A6000 GPU. |
| Software Dependencies | No | We used the Adam optimizer [55] with β1 = 0.9, β2 = 0.999 for all experiments. For the U-Net predicting the output leaves, we used an initial step size of 0.001. ... One can obtain an exact solution q to Eq. (A5) using standard methods for convex optimization, e.g. algorithms in CVXPY [63]. |
| Experiment Setup | Yes | We used the Adam optimizer [55] with β1 = 0.9, β2 = 0.999 for all experiments. For the U-Net predicting the output leaves, we used an initial step size of 0.001. For the MLP predicting leaf probability, however, we found it beneficial to use a smaller initial step size of 0.0002. ... For both components, the step size was reduced by a factor of 10 if the validation loss stagnated for more than 10 epochs, and the minimum step size was set to 5e-6. We used a batch size of 32 for 70 epochs for all tasks. |