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I am working on two problems:

  1. classification of images into high-level classes (e.g. shoe, dress, jacket etc.)
  2. classification of the attributes of the same images on a lower level (e.g. shoe style, color of the dress etc.), assuming that the high level class is known

Currently, I have designed an architecture for the 2nd problem as a multi-class multi-output network with ResNet50 as the backbone. Now I am dealing with the 1st problem and I have two paths to follow:

  1. consider both problems as different tasks and train separate models for them
  2. share the backbone between 1st and 2nd problems and train only one multi-output model

Which option is better? Which one should work better? Are there any good practices for combining similar tasks?

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If both the tasks consolidated classes set is small,using the multi-output network will be useful. But if your consolidated classes set is very large then I would suggest you go with different networks.

Personally, I would prefer to train individual models as it is hard to optimize multi-output networks.

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  • $\begingroup$ Thanks for the answer. I can assume that the dataset is big enough - do you think that the accuracy of multi-output model and separate models will be similar or one of them will be better? $\endgroup$ – DeeM Oct 12 '20 at 20:00

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