# What techniques exist to increase the learning importance of difficult-to-learn labels over easy ones?

I am training a model to place labels in image data. Some labels are learnt very quickly by the model while others take a long time to perfect. I cannot simply add more labeled data with only the labels I am looking to improve on since most of the image data contains a combination of the easy labels and the more difficult ones. Are there any smart ways to get the model to focus on the hard labels? I am just looking for some leads. Of course I can just train for longer but that seems inefficient as half the labels are already predicted almost perfectly.

• are we talking about low represented classes or balanced classes but some of them are not classified properly for unknown reasons? If it's the latter case it would be helpful to provide more information about the data and some examples. Jan 26 at 14:29
• The label set is balanced. Some labels are harder to place correctly on the image because they are simply more difficult to identify (since some features I am trying to label have more variance in shape/perspective, lighting conditions and motion). Jan 26 at 14:38
• if those are the main issues sounds like you need just some extra prerocessing steps to standardize your images and remove that variance. But again, hard to give suggestions without concrete examples. Jan 26 at 14:47
• Well I am tracking animals frame by frame. Some bodyparts are easy to identify (eg: all the points along spine from head to tail) whereas limbs are harder because they can obscure each other and move quickly. A left front paw and right front paw can look very different depending on where is it seen from whereas the neck or top of the tail is trivial to find. Jan 26 at 15:43

• Self-Paced learning, the core idea is to replace the classic loss with an objective function that can learn both, model weights and a parameter (usually indicated with $$v$$) indicating the difficulty of each training instance.