Jian Sun
Computer vision researcher and former Chief Scientist of Megvii, previously at Microsoft Research Asia; co-author of ResNet and Faster R-CNN. Died in 2022; this page reflects his complete public record.
Jian Sun did not write this page. ppll compiled it from their own words, each linked to the source. Is this you? · Report a mistake
What they believe
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Depth is not free: past a point, deeper neural networks are harder to train rather than simply better.
Deeper neural networks are more difficult to train.
arxiv.orgSaid 10 Dec 2015This on korrents
The bold line is korrents’ wording; the quote is theirs. These one: what they wrote before what they said aloud, one per source, newest first.
Beliefs
Korrents What they believe 4 beliefs — each backed by an exact quote.
Each is a — compiled by korrents.com, not by them: the one-line wordings are korrents', the quotes are theirs.
Recent
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better.
Deeper neural networks are more difficult to train.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases.
We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Show 1 more
The depth of a representation, not any particular architecture trick, is what most visual recognition tasks turn on.
The depth of representations is of central importance for many visual recognition tasks.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Beliefs others hold too
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. 2 hold this
Deeper neural networks are more difficult to train.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. 2 hold this
We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases. 2 hold this
We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
He, Zhang, Ren & Sun, "Deep Residual Learning for Image Recognition" (arXiv) Said 10 Dec 2015
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