MobileNetV2

Integrated Gradients BOOST Knowledge Distillation

Knowledge Distillation Meets Integrated Gradients: A Smarter Way to Compress Neural Networks

Imagine watching someone take an expert’s detailed reasoning, strip out everything except the most important cues, and hand those cues to a student who has never seen the full picture. That is roughly what a team from National Cheng Kung…

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SSD-KD: A Compact Skin Lesion Classifier That Outperforms Its Own Teacher Model

SSD-KD: A Compact Skin Lesion Classifier That Outperforms Its Own Teacher Model

Skin cancer detection has become one of the clearer success stories for deep learning in medicine, with models trained on large dermoscopy image collections repeatedly matching or approaching dermatologist level accuracy on benchmark datasets. The catch is that the models…

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Illustration of the framework of the proposed method. In the first stage, an adversarial image is processed with multiscale analysis: the image will be downsampled by a factor of 1/2 and 1/4, respectively, and upsampled by a factor of 2. Then in the second stage, we design and insert 𝑁 diffusive and denoising aggregation mechanism (DDA) blocks sequentially. Each DDA block involves a diffusive process (Section 3.2), a denoising process (Section 3.3), and an aggregation process (Section 3.4). The output samples from the last DDA block will be inversely processed to the original scale and smoothed to obtain the reversed image.

Reversing Adversarial Attacks on Skin Cancer AI With Multiscale Noise

Skin cancer screening was one of the first places deep learning actually earned its hype. A 2017 Nature paper by Esteva and colleagues showed a convolutional network matching board certified dermatologists at distinguishing benign moles from malignant ones, and the…

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