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I have an LSTM model for action recognition. During inference, any random actions that are not labelled or the model has not learned at all are also predicted with very high confidence score. I checked label smooothing technique, which will reduce the confidence score of overconfident model. Still, the score of wrongly predicted samples are quite high. How can we solve such problem?

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If you haven't stratified the splot of train/val/test sets, I would suggest checking

  • class ratio in train set
  • class ration in val set
  • class ration in test set
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