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That's only partially true. Deep learning approaches for segmentation are state of the art where you have large labeling sets with high variability in the targ
by ska 7y ago
That's only partially true. Deep learning approaches for segmentation are state of the art where you have large labeling sets with high variability in the targets, but that hardly covers everything. The techniques are also being used where this isn't the case, of course, but with mixed results at best. Which should hardly be surprising, as transfer methods are hardly a panacea.
- bonoboTP 7y agoCan you give an example where a non-deep learning segmentation method outperforms deep learning algorithms? Do you mean static camera setups where traditional background subtraction works well enough? I would assume DL approaches match or outperform those even there.
- deleted 7y ago[deleted]
- ska 7y agoYou can't just look at segmentation tasks, but the availability of good labeling and sufficient training data. Given enough data of sufficient quality capturing the variability of your domain, and good quality labeling on it, you can probably make a deep model perform quite well. Failing to have these inputs, you will often do better with other techniques (there are many). Furthermore, if you have a decent physical model and/or some constraints (e.g. lots of industrial QA, cell counting, etc.) with a fixed FOV etc., you can do quite well with classic approaches and that can be quite robust. Some of the deep models you see performing well on e.g. small curated sets for conferences just don't generalize well at all, which isn't surprising given the setup.