Sammenlign metoder

	Forklarlig Billedklassifikation	Finjusteret billedklassifikation
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2016-2017	2010–2014
Ophavsperson	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)	Yosinski, J. et al.; Pan, S. J. & Yang, Q.
Type	Post-hoc explainability applied to image classifiers	Transfer learning / fine-tuning
Oprindelig kilde	Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618-626.	Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems (NeurIPS), 27, 3320–3328.
Aliasser	XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition	fine-tuning for image recognition, transfer learning image classifier, pretrained CNN fine-tuning, domain-specific image classifier
Relaterede	4	5
Resumé	Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is to make the classifier's decision process transparent, auditable, and trustworthy.	Fine-tuned image classification adapts a large neural network pretrained on a broad image corpus (such as ImageNet) to a specific target domain by continuing training on labeled domain images. This approach achieves strong accuracy with far fewer target-domain samples than training from scratch, making it the dominant paradigm for applied computer vision tasks.