Curriculum Learning is a training scheme where we train a deep learning model on easy samples first and gradually increase the difficulty level of the training samples. It is an effective way of preventing the model from getting trapped in a suboptimal solution, which can happen if it is trained only on difficult samples. The idea behind curriculum learning is that it mimics how humans learn new concepts; we start with the basics and move on to more complex topics.
Curriculum Learning can be applied in Keras models by progressively increasing the difficulty of the training samples. This can be achieved by sorting the training samples based on their difficulty level using some pre-defined metrics. For example, in the case of object recognition, we can sort the training samples based on their object size. We can start training the model on the small objects first and slowly increase the size of the objects.
In Keras, we can implement curriculum learning by defining a custom data generator for our training data. We can then use this generator to load the training samples in a sorted order based on their difficulty level. We can use the ‘sample_weight‘ parameter in Keras to assign weights to each sample based on its difficulty level. This will ensure that the model pays more attention to the difficult samples during training.
Another approach to implementing curriculum learning in Keras is by using transfer learning. In transfer learning, we initially train the model on a smaller set of easy samples and then fine-tune it on harder samples. This way, the model learns a good initial representation of the problem and then optimizes it for more complex variations.
Overall, curriculum learning can be a powerful tool for training deep learning models, especially in cases where the data instances differ significantly in difficulty level. By starting with easy samples and gradually increasing the difficulty level, we can ensure that the model learns a more robust and representative representation of the problem.