Continual learning aims to create artificial neural networks capable of
accumulating knowledge and skills through incremental training on a sequence of
tasks. The main challenge of continual learning is catastrophic interference,
wherein new knowledge overrides or interferes with past knowledge, leading to
forgetting. An associated issue is the problem of learning "cross-task
knowledge," where models fail to acquire and retain knowledge that helps
differentiate classes across task boundaries. A common solution to both
problems is "replay," where a limited buffer of past instances is utilized to
learn cross-task knowledge and mitigate catastrophic interference. However, a
notable drawback of these methods is their tendency to overfit the limited
replay buffer. In contrast, our proposed solution, SurpriseNet, addresses
catastrophic interference by employing a parameter isolation method and
learning cross-task knowledge using an auto-encoder inspired by anomaly
detection. SurpriseNet is applicable to both structured and unstructured data,
as it does not rely on image-specific inductive biases. We have conducted
empirical experiments demonstrating the strengths of SurpriseNet on various
traditional vision continual-learning benchmarks, as well as on structured data
datasets. Source code made available at this https URL
and this https URL

SurpriseNet 是一种解决持续学习中灾难性干扰和跨任务知识学习问题的方法，通过参数隔离和基于异常检测的自编码器来处理，适用于结构化和非结构化数据。

看我，无重放！SurpriseNet：受启发的无监督增量学习的异常检测

Look At Me, No Replay! SurpriseNet: Anomaly Detection Inspired Class  Incremental Learning

Prompt Tuning, conditioning on task-specific learned prompt vectors, has
emerged as a data-efficient and parameter-efficient method for adapting large
pretrained vision-language models to multiple downstream tasks. However,
existing approaches usually consider learning prompt vectors for each task
independently from scratch, thereby failing to exploit the rich shareable
knowledge across different vision-language tasks. In this paper, we propose
multitask vision-language prompt tuning (MVLPT), which incorporates cross-task
knowledge into prompt tuning for vision-language models. Specifically, (i) we
demonstrate the effectiveness of learning a single transferable prompt from
multiple source tasks to initialize the prompt for each target task; (ii) we
show many target tasks can benefit each other from sharing prompt vectors and
thus can be jointly learned via multitask prompt tuning. We benchmark the
proposed MVLPT using three representative prompt tuning methods, namely text
prompt tuning, visual prompt tuning, and the unified vision-language prompt
tuning. Results in 20 vision tasks demonstrate that the proposed approach
outperforms all single-task baseline prompt tuning methods, setting the new
state-of-the-art on the few-shot ELEVATER benchmarks and cross-task
generalization benchmarks. To understand where the cross-task knowledge is most
effective, we also conduct a large-scale study on task transferability with 20
vision tasks in 400 combinations for each prompt tuning method. It shows that
the most performant MVLPT for each prompt tuning method prefers different task
combinations and many tasks can benefit each other, depending on their visual
similarity and label similarity. Code is available at
this https URL.

本文提出了一种多任务视觉语言提示调整（MVLPT）方法，将跨任务知识纳入提示调整算法中，其结果在 20 个视觉任务上的表现优于现有方法。