Big-model Driven Few-shot Continual Learning

• URL: http://arxiv.org/abs/2309.00862v1
• Date: Sat, 2 Sep 2023 08:39:46 GMT
• Title: Big-model Driven Few-shot Continual Learning
• Authors: Ziqi Gu and Chunyan Xu and Zihan Lu and Xin Liu and Anbo Dai and Zhen Cui
• Abstract summary: We propose a Big-model driven Few-shot Continual Learning (B-FSCL) framework to gradually evolve the model. Experimental results of our proposed B-FSCL on three popular datasets completely surpass all state-of-the-art FSCL methods.
• Score: 24.392821997721295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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