Cascade learning is a type of ensemble learning, and it is a multi-stage that concatenates several learning models. Deep Cascade Learning (DCL) comprises layers of deep neural networks trained in a cascade manner. The significance of deep cascade learning is the ability to tackle the vanishing gradient problem. The advantages of layer-wise training in deep cascade learning are reducing the training time complexity and using less memory.
The cascade correlation algorithm uses a convolutional neural network(CNN), long short-term memory(LSTM) and allows to train a CNN in a bottom-up layer-by-layer manner. Deep cascade learning algorithm divides networks into layers and trains each layer one by one until all the layers get trained. Development on deep cascade learning is cascade transfer learning which is cascading the pre-trained networks. It is similar to deep cascade learning, and it adapts pre-trained networks layer by layer. It is also applied in the combination of deep cascade learning. Applications of DCL are computer vision and human activity recognition. Future application areas of DCL are Speech and Natural Language Processing, domain adaptation, and many more.
• Inspired by the cascade correlation algorithm, Deep cascade learning has been developed, which is the efficient training of deep neural networks in a bottom-up fashion using a layered structure.
• In cascade learning, the training of deep networks circumvents the vanishing gradient problem by ensuring that the output is always adjacent to the layer being trained.
• As every trainable layer is immediately adjacent to the output block in cascade learning, it helps the network to obtain more robust representations at every layer.
• Deep cascade learning reduces the complexity of training over end-to-end learning, both training time and memory, and achieves outstanding performance on several machine learning tasks.
• Moreover, it has the potential to find the number of layers required to fit a certain problem (adaptive architecture), similar to the cascade correlation and AdaNet.