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

Ada-QPacknet -- adaptive pruning with bit width reduction as an  efficient continual learning method without forgetting

Indiscriminate data poisoning attacks are quite effective against supervised
learning. However, not much is known about their impact on unsupervised
contrastive learning (CL). This paper is the first to consider indiscriminate
poisoning attacks of contrastive learning. We propose Contrastive Poisoning
(CP), the first effective such attack on CL. We empirically show that
Contrastive Poisoning, not only drastically reduces the performance of CL
algorithms, but also attacks supervised learning models, making it the most
generalizable indiscriminate poisoning attack. We also show that CL algorithms
with a momentum encoder are more robust to indiscriminate poisoning, and
propose a new countermeasure based on matrix completion. Code is available at:
this https URL.

本文针对无监督对比学习中的不分靶标数据污染攻击进行了研究。该研究首次提出了 Contrastive Poisoning，这是一种破坏性最强、最具一般性的不分靶标攻击方式。同时针对这种攻击，提出了一种基于矩阵填充的新型对策。实验证明，强调数据惯性的编码器可以更加有效地抵御这种攻击。