Published as a conference paper at ICLR 2018 FEARNET: BRAIN-INSPIRED MODEL FOR INCREMENTAL LEARNING
Incremental class learning involves sequentially learning classes in bursts of examples from the same class. This violates the assumptions that underlie methods for training standard deep neural networks, and will cause them to suffer from catastrophic forgetting. Arguably, the best method for incremental class learning is iCaRL, but it requires storing training examples for each class, making it challenging to scale. Here, we propose FearNet for incremental class learning. FearNet is a generative model that does not store previous examples, making it memory efficient. FearNet uses a brain-inspired dual-memory system in which new memories are consolidated from a network for recent memories inspired by the mammalian hippocampal complex to a network for long-term storage inspired by medial prefrontal cortex. Memory consolidation is inspired by mechanisms that occur during sleep. FearNet also uses a module inspired by the basolateral amygdala for determining which memory system to use for recall. FearNet achieves state-of-the-art performance at incremental class learning on image (CIFAR-100, CUB-200) and audio classification (AudioSet) benchmarks.
Quality: The paper presents a novel solution to an incremental classification problem based on a dual memory system. The proposed solution is inspired by the memory storage mechanism in brain.
Clarity: The problem has been clearly described and the proposed solution is described in detail. The results of numerical experiments and the real data analysis are satisfactory and clearly shows the superior performance of the method compared to the existing ones.
Originality: The solution proposed is a novel one based on a dual memory system inspired by the memory storage mechanism in brain. The memory consolidation is inspired by the mechanisms that occur during sleep. The numerical experiments showing the FearNet performance with sleep frequency also validate the comparison with the brain memory system.
Significance: The work discusses a significant problem of incremental classification. Many of the shelf deep neural net methods require storage of previous training samples too and that slows up the application to larger dataset. Further the traditional deep neural net also suffers from the catastrophic forgetting. Hence, the proposed work provides a novel and scalable solution to the existing problem.
pros: (a) a scalable solution to the incremental classification problem using a brain inspired dual memory system
          (b) mitigates the catastrophic forgetting problem using a memory consolidation by pseudorehearsal.
          (c) introduction of a subsystem that allows which memory system to use for the classification
cons: (a)  How FearNet would perform if imbalanced classes are seen in more than one study sessions?
          (b) Storage of class statistics during pseudo rehearsal could be computationally expensive. How to cope with that?
          (c) How FearNet would handle if there are multiple data sources?