Another inter- esting insight is that a smaller colour gamut has an impact on the way the deep neural networks perceive the image and results in a drop of accuracy due to the shift in distribution of the image. Presence of additive colour noise also reduces the performance of the classifiers.
The main aim of this paper is to study the impact of colour distortions on the performance of image classification using deep neural networks.
Sep 2, 2022 · The aim of our work is to study the impact of color variation on the performance of DNNs. We perform experiments on several state-of-the-art DNN ...
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The main aim of this paper is to study the impact of colour distortions on the performance of image classification using deep neural networks. Experiments ...
The main aim of this paper is to study the impact of colour distortions on the performance of image classification using deep neural networks using multiple ...
These color shifts result in a poor generalization of deep learning-based methods from the training domain to external pathology data. To increase test ...
The main aim of this paper is to study the impact of colour distortions on the performance of image classification using deep neural networks. Experiments ...
May 23, 2023 · Our experimental results demonstrate that these techniques can enhance the robustness of DNNs against brightness variation, leading to improved ...
The aim of our work is to study the impact of color variation on the performance of DNNs. We perform experiments on several state-of-the-art DNN architectures ...
pact of colour on the robustness of deep neural networks where they have synthetically generated colour distorted images using the publicly available ...