In this episode, we explore how Scheduled Sampling helps Recurrent Neural Networks (RNNs) make better predictions for tasks like machine translation and image captioning. Normally, during training, RNNs use the actual previous word or token to predict the next one. But when making predictions, the model has to use its own previous predictions, which can lead to mistakes building up. Scheduled Sampling solves this by slowly shifting the model from using the correct token during training to using its own predictions, helping it learn more effectively and reduce errors. Tune in to learn how this approach helped improve results in a major image captioning competition!
Link to research paper-
https://arxiv.org/abs/1506.03099
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