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Opening the Black-Box of Deep Learning in Image Classification
Deep learning algorithms are very good at recognizing specific objects (e.g. a dog, a car) within an image (known as image classifiers). But how do they actually do that? Most often the mechanisms underlying an algorithm’s decision remain opaque. What if we could explain any such black-box algorithm intuitively and, by doing so, even learn from it?
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