CLR：面向持续学习的通道级轻量重编程
CLR: Channel-wise Lightweight Reprogramming for Continual Learning
AI总结：
针对持续学习中的灾难性遗忘问题，提出通道级轻量重编程方法，通过共享不可变锚点参数加任务专属轻量重编程参数实现稳定性与可塑性的平衡，参数增量低于0.6%，在53个图像分类数据集序列上优于13种现有基准。
摘要：
持续学习旨在模拟人类在连续任务中不断积累知识的能力，其核心挑战是在学习新任务后保持已学任务的性能，即避免灾难性遗忘。我们提出通道级轻量重编程（Channel-wise Lightweight Reprogramming，CLR）方法，帮助卷积神经网络（CNNs）在持续学习过程中克服灾难性遗忘。研究表明，针对旧任务（或自监督代理任务）训练的CNN模型，可通过我们提出的轻量（极低开销）重编程参数被“重编程”以解决新任务。借助CLR，我们能在稳定性与可塑性之间取得更优权衡以解决持续学习问题：为维持稳定性并保留先前任务的能力，我们采用通用的任务无关不可变部分作为共享“锚点”参数集；随后添加任务专属的轻量重编程参数来重新解读不可变部分的输出，从而实现可塑性并整合新知识。学习连续任务时，我们仅需训练轻量重编程参数来学习每个新任务。重编程参数是任务专属且各任务互斥的，这使得我们的方法对灾难性遗忘具有免疫力。为最小化学习新任务时重编程的参数需求，我们通过仅调整核心卷积核，并学习从锚点参数到任务专属领域知识的通道级线性映射，实现重编程的轻量化。研究表明，对于通用CNN，任意新任务带来的CLR参数增量均低于0.6%。在一个包含53个图像分类数据集的新型挑战性序列上，我们的方法性能优于13种最先进的持续学习基准方法。代码和数据可在https://github.com/gyhandy/Channel-wise-Lightweight-Reprogramming获取。
英文摘要：
Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks. The main challenge is to maintain performance on previously learned tasks after learning new tasks, i.e., to avoid catastrophic forgetting. We propose a Channel-wise Lightweight Reprogramming (CLR) approach that helps convolutional neural networks (CNNs) overcome catastrophic forgetting during continual learning. We show that a CNN model trained on an old task (or self-supervised proxy task) could be ``reprogrammed" to solve a new task by using our proposed lightweight (very cheap) reprogramming parameter. With the help of CLR, we have a better stability-plasticity trade-off to solve continual learning problems: To maintain stability and retain previous task ability, we use a common task-agnostic immutable part as the shared ``anchor" parameter set. We then add task-specific lightweight reprogramming parameters to reinterpret the outputs of the immutable parts, to enable plasticity and integrate new knowledge. To learn sequential tasks, we only train the lightweight reprogramming parameters to learn each new task. Reprogramming parameters are task-specific and exclusive to each task, which makes our method immune to catastrophic forgetting. To minimize the parameter requirement of reprogramming to learn new tasks, we make reprogramming lightweight by only adjusting essential kernels and learning channel-wise linear mappings from anchor parameters to task-specific domain knowledge. We show that, for general CNNs, the CLR parameter increase is less than 0.6\% for any new task. Our method outperforms 13 state-of-the-art continual learning baselines on a new challenging sequence of 53 image classification datasets. Code and data are available at https://github.com/gyhandy/Channel-wise-Lightweight-Reprogramming
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