Continual Learning (CL) is a process in which there is still huge gap between human and deep learning model efficiency. Recently, many CL algorithms were designed. Most of them have many problems with learning in dynamic and complex environments. In this work new architecture based approach Ada-QPacknet is described. It incorporates the pruning for extracting the sub-network for each task. The crucial aspect in architecture based CL methods is theirs capacity. In presented method the size of the model is reduced by efficient linear and nonlinear quantisation approach. The method reduces the bit-width of the weights format. The presented results shows that hybrid 8 and 4-bit quantisation achieves similar accuracy as floating-point sub-network on a well-know CL scenarios. To our knowledge it is the first CL strategy which incorporates both compression techniques pruning and quantisation for generating task sub-networks. The presented algorithm was tested on well-known episode combinations and compared with most popular algorithms. Results show that proposed approach outperforms most of the CL strategies in task and class incremental scenarios.

利用Ada-QPacknet的新型架构方法，在动态和复杂环境中学习，结合了模型的大小缩减和量化减少权重格式位数的技术，在连续学习场景中实现了与浮点子网络相似准确性的8位和4位混合量化，并在任务和类增量场景中优于大多数连续学习策略。

Ada-QPacknet -- 无遗忘的高效持续学习方法，带自适应剪枝与位宽缩减