Let's consider one has built a fully-supervised neural network for some task, e.g. localizing an object in various scenes. As you can imagine, it is quite time-consuming to label data: one has to manually localize the object in an image and draw a bounding-box around it - one at a time.

Say we have a normal convolutional neural network (CNN) for the fully-supervised localization, thus something like this:

          2D input image
       convolutional-layer 1
       convolutional-layer N
      ---flattened output---
    fully-connected classifier
           output layer

How can we adapt this architecture to utilize weakly-labelled data, too?

No matter if we have a sliding-window approach, something like OverFeat or probability distributions for actually localizing objects: we always need fully-labelled training data. This is a problem, because labelling data fully-supervised is really time consuming. Hence, fully-labelled data sets are pretty rare.

In contrast to this, typically large amounts of weakly-labelled or unlabeled data exists. In my opinion there is a large potential in weakly-labelled / unlabelled data, due to its vast availability. The problem is using this potential without having to manually label every sample by hand.

That said, my question is: how can weakly-labelled data sets (i.e. "object" vs. "no object") or completely unlabelled data sets be utilized to improve the robustness of fully-supervised architectures, like the mentioned CNN?

Is it generally possible to mix a supervised approach with some unsupervised approach? Like enabling a fully-supervised architecture to somehow utilize weakly-labelled data for training?


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