Starting your first architecture job can be a bit like graduating from primary school to secondary college. Your hard work and dedication at university may have you feeling somewhat confident with your skills, but this can evaporate in a flash on your first day at work.
Non-linear activation layers are employed after all layers with weights (so-called learnable layers, such as FC layers and convolutional layers) in CNN architecture. This non-linear performance of the activation layers means that the mapping of input to output will be non-linear; moreover, these layers give the CNN the ability to learn extra-complicated things. The activation function must also have the ability to differentiate, which is an extremely significant feature, as it allows error back-propagation to be used to train the network. The following types of activation functions are most commonly used in CNN and other deep neural networks.
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This is defined as incorporating new information into a plain DL model, made possible by interfering with the learned information. For instance, consider a case where there are 1000 types of flowers and a model is trained to classify these flowers, after which a new type of flower is introduced; if the model is fine-tuned only with this new class, its performance will become unsuccessful with the older classes [183, 184]. The logical data are continually collected and renewed, which is in fact a highly typical scenario in many fields, e.g. Biology. To address this issue, there is a direct solution that involves employing old and new data to train an entirely new model from scratch. This solution is time-consuming and computationally intensive; furthermore, it leads to an unstable state for the learned representation of the initial data. At this time, three different types of ML techniques, which have not catastrophic forgetting, are made available to solve the human brain problem founded on the neurophysiological theories [185, 186]. Techniques of the first type are founded on regularizations such as EWC [183] Techniques of the second type employ rehearsal training techniques and dynamic neural network architecture like iCaRL [187, 188]. Finally, techniques of the third type are founded on dual-memory learning systems [189]. Refer to [190,191,192] in order to gain more details. 2ff7e9595c
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