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This Looks Like That: Deep Learning for Interpretable Image Recognition When we are faced with challenging image classification tasks, we often explain our reasoning by dissecting the image, and pointing out prototypical aspects of one class or another The mounting evidence for each of the classes helps us make our final decision
This looks like that | Proceedings of the 33rd International Conference . . . Our experiments show that ProtoPNet can achieve comparable accuracy with its analogous non-interpretable counterpart, and when several ProtoPNets are combined into a larger network, it can achieve an accuracy that is on par with some of the best-performing deep models
ProtoPNet this_looks_like_that. pdf at master - GitHub This code package implements the prototypical part network (ProtoPNet) from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition" (to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University), Chaofan Tao (Duke University), Alina Jade Barnett (Duke University), Jonathan Su (MIT
Deep learning - Wikipedia For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the analytic results to identify cats in other images
This Looks Like That: Deep Learning for Interpretable Image Recognition The model is able to identify several parts of the image where it thinks that this identified part of the image looks like that prototypical part of some training image, and makes its prediction based on a weighted combination of the similarity scores
What Is Deep Learning? Neural Networks, Applications Tools Explore the fundamentals of deep learning, from neural networks and deep neural architectures to real-world applications like AI writing, detection, vision, and more Learn how deep learning powers modern AI and which tools to use in 2025