Big-model Driven Few-shot Continual Learning
Ziqi Gu Chunyan Xu Zihan Lu Xin Liu Anbo Dai Zhen Cui
Few-shot continual learning (FSCL) has attracted intensive attention and
achieved some advances in recent years, but now it is difficult to again make a
big stride in accuracy due to the limitation of only few-shot incremental
samples. Inspired by distinctive human cognition ability in life learning, in
this work, we propose a novel Big-model driven Few-shot Continual Learning
(B-FSCL) framework to gradually evolve the model under the traction of the
world's big-models (like human accumulative knowledge). Specifically, we
perform the big-model driven transfer learning to leverage the powerful
encoding capability of these existing big-models, which can adapt the continual
model to a few of newly added samples while avoiding the over-fitting problem.
Considering that the big-model and the continual model may have different
perceived results for the identical images, we introduce an instance-level
adaptive decision mechanism to provide the high-level flexibility cognitive
support adjusted to varying samples. In turn, the adaptive decision can be
further adopted to optimize the parameters of the continual model, performing
the adaptive distillation of big-model's knowledge information. Experimental
results of our proposed B-FSCL on three popular datasets (including CIFAR100,
minilmageNet and CUB200) completely surpass all state-of-the-art FSCL methods.