  Inspired by the Lottery Ticket Hypothesis (LTH), which highlights the
existence of efficient subnetworks within larger, dense networks, a
high-performing Winning Subnetwork (WSN) in terms of task performance under
appropriate sparsity conditions is considered for various continual learning
tasks. It leverages pre-existing weights from dense networks to achieve
efficient learning in Task Incremental Learning (TIL) and Task-agnostic
Incremental Learning (TaIL) scenarios. In Few-Shot Class Incremental Learning
(FSCIL), a variation of WSN referred to as the Soft subnetwork (SoftNet) is
designed to prevent overfitting when the data samples are scarce. Furthermore,
the sparse reuse of WSN weights is considered for Video Incremental Learning
(VIL). The use of Fourier Subneural Operator (FSO) within WSN is considered. It
enables compact encoding of videos and identifies reusable subnetworks across
varying bandwidths. We have integrated FSO into different architectural
frameworks for continual learning, including VIL, TIL, and FSCIL. Our
comprehensive experiments demonstrate FSO's effectiveness, significantly
improving task performance at various convolutional representational levels.
Specifically, FSO enhances higher-layer performance in TIL and FSCIL and
lower-layer performance in VIL.
