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Papers/Reduction of Class Activation Uncertainty with Background ...

Reduction of Class Activation Uncertainty with Background Information

H M Dipu Kabir

2023-05-05Image ClassificationSatellite Image ClassificationClassificationFine-Grained Image Classification
PaperPDFCode(official)Code

Abstract

Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this paper, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecting background images and discuss potential future improvements. We apply our approach to several datasets and achieve improved generalization with much lower computation. Through the class activation mappings (CAMs) of the trained models, we observed the tendency towards looking at a bigger picture with the proposed model training methodology. Applying the vision transformer with the proposed background class, we receive state-of-the-art (SOTA) performance on CIFAR-10C, Caltech-101, and CINIC-10 datasets. Example scripts are available in the `CAM' folder of the following GitHub Repository: github.com/dipuk0506/UQ

Results

TaskDatasetMetricValueModel
Image ClassificationCIFAR-10Percentage correct99.05VIT-L/16 (Spinal FC, Background)
Image ClassificationCINIC-10Accuracy95.8VIT-L/16 (Spinal FC, Background)
Image ClassificationFlowers-102Accuracy99.75VIT-L/16 (Background)
Image ClassificationCIFAR-100Percentage correct93.31VIT-L/16 (Spinal FC, Background)
ClassificationCIFAR-10CAccuracy on Brightness Corrupted Images99.03ViT-L/16 (Background)

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