A biologically inspired dual-network memory model for reduction of catastrophic forgetting

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Continual Learning with Deep Generative Replay (2017, background): “A more recent work proposes an architecture that resembles the structure of the hippocampus to facilitate continual learning for more complex data such as small binary pixel images [15].”
EEC: Learning to Encode and Regenerate Images for Continual Learning (2021, methodology): “Hattori (2014) uses autoencoders on binary pixel images and Kemker & Kanan (2018) (FearNet) uses a network pre-trained on ImageNet to extract feature embeddings for images, applying the autoencoder to the feature embeddings.”
Continual Learning with Adaptive Weights (CLAW) (2019, methodology): “(c) The memory-based approach relies on episodic memory to store data (or pseudo-data) from previous tasks (Ratcliff, 1990; Robins, 1993; 1995; Thrun, 1996; Schmidhuber, 2013; Hattori, 2014; Mocanu et al., 2016; Rebuffi et al., 2017; Kamra et al., 2017; Shin et al., 2017; Rolnick et al., 2018; van d”