Deep neural networks often severely forget previously learned knowledge when
learning new knowledge. Various continual learning (CL) methods have been
proposed to handle such a catastrophic forgetting issue from different
perspectives and achieved substantial improvements.In this paper, a novel
two-branch continual learning framework is proposed to further enhance most
existing CL methods. Specifically, the main branch can be any existing CL model
and the newly introduced side branch is a lightweight convolutional network.
The output of each main branch block is modulated by the output of the
corresponding side branch block. Such a simple two-branch model can then be
easily implemented and learned with the vanilla optimization setting without
whistles and bells.Extensive experiments with various settings on multiple
image datasets show that the proposed framework yields consistent improvements
over state-of-the-art methods.

通过引入轻量级卷积网络的两分支连续学习框架，实现对深度神经网络中灾难性遗忘问题的改善和性能提升。

图像类别增量学习通用双分支框架

Generalizable Two-Branch Framework for Image Class-Incremental Learning

We propose a high-quality photo-to-pencil translation method with
fine-grained control over the drawing style. This is a challenging task due to
multiple stroke types (e.g., outline and shading), structural complexity of
pencil shading (e.g., hatching), and the lack of aligned training data pairs.
To address these challenges, we develop a two-branch model that learns separate
filters for generating sketchy outlines and tonal shading from a collection of
pencil drawings. We create training data pairs by extracting clean outlines and
tonal illustrations from original pencil drawings using image filtering
techniques, and we manually label the drawing styles. In addition, our model
creates different pencil styles (e.g., line sketchiness and shading style) in a
user-controllable manner. Experimental results on different types of pencil
drawings show that the proposed algorithm performs favorably against existing
methods in terms of quality, diversity and user evaluations.

本文提出一种高质量的照片转铅笔图像翻译方法，并通过双分支模型实现绘画风格的精细控制、创建用户可控制的不同铅笔风格以及通过图像滤波技术提取干净的笔画和根据手工标记的绘画样式生成训练数据对。实验结果表明，与现有方法相比，该方法在图像质量、多样性和用户评估方面表现优异。