Multiscale Score Matching for Out-of-Distribution Detection
Authors: Ahsan Mahmood, Junier Oliva, Martin Andreas Styner
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Despite its simplicity, our experiments show that this methodology significantly outperforms the state-of-the-art in detecting out-of-distribution images. |
| Researcher Affiliation | Academia | Department of Computer Science University of North Carolina at Chapel Hill {amahmood, joliva, styner}@cs.unc.edu |
| Pseudocode | No | The paper describes its methods in narrative text and mathematical equations but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make our code and results publicly available on Github 1. 1https://github.com/ahsan Mah/msma |
| Open Datasets | Yes | We consider CIFAR-10 (Krizhevsky et al. (2009)) and SVHN (Netzer et al. (2011)) as our inlier datasets. For out-of-distribution datasets, we choose the same as Liang et al. (2017): Tiny Image Net3, LSUN (Yu et al. (2015)), i SUN (Xu et al. (2015)). ...We employ 3500 high resolution T1-weighted MR images obtained through the NIH large scale ABCD study (Casey et al. (2018))... For our outlier data, we employ MRI datasets of children aged 1, 2, 4 and 6 years (500 each) from the UNC EBDS database Stephens et al. (2020); Gilmore et al. (2020). |
| Dataset Splits | Yes | CIFAR-10: ...There are 50,000 training images and 10,000 test images. SVHN: ...We use the official splits: 73,257 digits for training, 26,032 digits for testing. For our Gaussian Mixture Models, we mean normalize the data and perform a grid search over the number of components (ranging from 2 to 20), using 10-fold cross validation. |
| Hardware Specification | No | The paper mentions 'GPU memory usage' but does not provide specific hardware details such as GPU models, CPU types, or memory amounts. |
| Software Dependencies | No | The paper mentions 'TensorFlow implementation' and 'Keras' but does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | The model architecture used is a Refine Net with 128 filters. The batch size is also fixed to 128. We train for 200k iterations using the Adam optimizer. Following Song & Ermon (2019), we use L = 10 standard deviations for our Gaussian noise perturbation such that {σi}L i=1 is a geometric sequence with σ1 = 1 and σ10 = 0.01. ...For our Gaussian Mixture Models, we mean normalize the data and perform a grid search over the number of components (ranging from 2 to 20), using 10-fold cross validation. Our normalizing flow model is constructed with a MAF using two hidden layers with 128 units each, and a Standard Normal as the base distribution. It is trained for a 1000 epochs with a batch size of 128. |