Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++
Authors: Barath Mohan Umapathi, Kushal Chauhan, Pradeep Shenoy, Devarajan Sridharan
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our approaches extensively with five grayscale and six natural image datasets and show that they achieve or exceed stateof-the-art outlier detection, particularly on datasets with complex, natural images. |
| Researcher Affiliation | Collaboration | Barath Mohan Umapathi1 , Kushal Chauhan2 , Pradeep Shenoy2 and Devarajan Sridharan3,4, 1Department of Physics, Indian Institute of Science 2Google Research 3Center for Neuroscience, Indian Institute of Science 4Computer Science and Automation, Indian Institute of Science |
| Pseudocode | No | The paper describes methods in prose and figures but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code associated with this paper is available at: https://github. com/coglabiisc/googleresearch/tree/main/pixelcnn ood |
| Open Datasets | Yes | We trained Pixel CNN++ models on each of five grayscale image datasets: MNIST, Fashion MNIST, EMNIST Letters, Sign Language MNIST, and CLEVR [Deng, 2012; Xiao et al., 2017; Cohen et al., 2017; Johnson et al., 2017; Sign Language MNIST, 2017]. Similarly, we trained Pixel CNN++ models on each of six natural image datasets SVHN, Celeb A, Comp Cars, GTSRB, CIFAR10, and LSUN (classroom) [Netzer et al., 2011; Liu et al., 2015; Yang et al., 2015; Stallkamp et al., 2011; Krizhevsky, 2009; Yu et al., 2015]. |
| Dataset Splits | No | The paper mentions training and test sets and sometimes refers to training log-likelihoods, but does not explicitly provide details on how the datasets were split into training, validation, and test sets (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running experiments. It mentions time and space complexity in Table 1 but no specific hardware. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | No | The paper does not explicitly detail hyperparameter values, optimization settings, or other specific experimental setup configurations in the main text. |