iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection
Authors: Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, Insup Lee7104-7114
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of i DECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that i DECODe can detect adversarial examples. |
| Researcher Affiliation | Collaboration | Ramneet Kaur1, Susmit Jha2, Anirban Roy2, Sangdon Park13, Edgar Dobriban4, Oleg Sokolsky1, Insup Lee1 1 Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA 2 Computer Science Laboratory, SRI International, Menlo Park, USA 3 School of Computer Science, Georgia Institute of Technology 4 Statistics & Computer Science, University of Pennsylvania |
| Pseudocode | Yes | Algorithm 1: i D Equivariance for Conformal OOD Detection |
| Open Source Code | Yes | Code, pre-trained models, and data are available at https://github.com/ramneetk/i DECODe. |
| Open Datasets | Yes | We demonstrate the efficacy of i DECODe by experiments on image and audio datasets... ablations studies on CIFAR-10 as the i D dataset and SVHN, LSUN, Image Net, CIFAR100, and Places365 datasets as OOD... FSDNoisy18k (FSD) (Fonseca et al. 2019) is an audio dataset |
| Dataset Splits | Yes | Here, the training set is split into a proper training set Xtr = {xi : i = 1, . . . , m} and a calibration set Xcal = {xj : j = m+1, . . . , l}... Calibration set of size 1000 images is randomly sampled with replacement from a held-out (not used in training) set of 5000 images... train the AVT model with WRN architecture on the proper training set (90% of the total training set) of one class of CIFAR-10... train the AVT model with Res Net-18 architecture on the proper training set (90% of the total training set) of CIFAR-100. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using frameworks like ResNet and WRN architectures, and implies PyTorch due to the code base URL, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The details about the AVT model M with Res Net architecture trained to be G-equivariant on the proper training set (90% of the total training data) of CIFAR-10 (with the same set of hyperparameters from Qi et al. (2019) s AVT model on CIFAR-10) are given in Appendix C.1.1. |