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> Also, it is interesting that they are not using deep learning for this. They are using a Discriminative Gaussian Process Latent Variable Model. It's my under
by hcarvalhoalves 12y ago
> Also, it is interesting that they are not using deep learning for this. They are using a Discriminative Gaussian Process Latent Variable Model.
It's my understanding that deep learning is more interesting when you have a large dataset and need to generalize (e.g., recognize faces sideways, upside-down, etc), otherwise, when the requirements are more predictable, the hand-tuned algorithms tend to perform better. The task of finding whether two pictures are from the same person doesn't seem appropriate for DL (compared to e.g. find whether a picture contains a person).
Would that be correct?
- apu 12y agoI'm not sure that's the distinction I'd use. To me, it seems more to be a matter of the kind and amount of training data you have. If you have a ton of labeled data (either for the exact task you're doing, or a related one), DL often wins because it's sufficiently high-capacity to learn sophisticated models from all that data. On the other hand, if you don't have enough data, then the deep network isn't going to be learnable, in which case hand-tuned methods might do better, depending on how much "domain expertise" you can bake into the system. Faces are a very constrained domain, and since there has been enormous work on them for decades now, it's one area that non-DL algorithms probably still have somewhat of a fighting chance (until people gather & label large enough databases, of course).
- hcarvalhoalves 12y agoThat makes more sense, thank you.