1 概念 在持续学习领域,Task incremental、Domain incremental、Class incremental 是三种主要的学习模式,它们分别关注不同类型的任务序列和数据分布变化。 1.1 Task Incremental Learning (Task-incremental) 任务增量学习,也称为任务增量式学习,是指在这种学习模式下,学习器依次面对不同的任务,每个任务有自己独特的类别集合。
这个挑战在域增量(Domain-incremental learning)问题上尤为明显,不同域的知识可能很难在同一个空间中共存。 在本工作中,我们打破成规提出一个双赢策略来解决域增量问题,通过学习跨域独立的Prompts使得模型在每个域都得到最佳性能而没有任何相互干扰,并将学习到的Prompts存储来消除灾难性遗忘问题。所提出的新的增量模式...
基于参数隔离的方法:为每个新任务添加额外参数到动态架构中。 多域增量学习 (Multi-Domain Incremental Learning) 多域增量学习中关于分类任务的工作包括:渐进神经网络、动态可扩展网络(DENs)、将控制模块连接到基础网络的方式。 最近的工作基于参数隔离技术将特定领域参数子集用于每个任务,但主要侧重于分类问题。与本任务...
Finally, we also formulate a more natural continual learning setting for medical imaging using a tapered uniform distribution schedule with gradual interleaved domain shifts.Srivastava, ShikharYaqub, MohammadNandakumar, KarthikGe, ZongyuanMahapatra, Dwarikanath...
Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit RecognitionAs affective robots become integral in human life, these agents must be able to fairly evaluate human affective expressions... O Kara,N Churamani,H Gunes - ACM 被引量: 0发表: ...
Despite the recent progress in incremental learning, addressing catastrophic forgetting under distributional drift is still an open and important problem. Indeed, while state-of-the-art domain incremental learning (DIL) methods perform satisfactorily within known domains, their performance largely degrades ...
Code for NeurIPS 2022 paper “S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning“ - GitHub - iamwangyabin/S-Prompts: Code for NeurIPS 2022 paper “S-Prompts Learning with Pre-trained Transformers: An Occa
Learning Transferable Features with Deep Adaptation Networks 3. Domain-A...Unsupervised Domain Adaptation by Backpropagation Unsupervised Domain Adaptation by Backpropagation 顶级性能的深层体系结构是在大量标记数据上训练的。在某项任务没有标记数据的情况下,域适配(domain adaptation)通常提供了一个有吸引力的...
In this paper, we show how typical issues of domain-incremental learning can be equally addressed with the properties of quantum mechanics, until to offer often better results. We propose the frameworkQUARTAto combine algorithms of quantum supervised learning, that is, variational quantum circuits, ...
在WACV 2024发表Cross-Domain Few-Shot Incremental Learning For Point-Cloud Recognition HAIVLab 华中科技大学 人工智能与自动化学院 HAIV Lab当无法获取二维图像时,感知三维物体对于机器人至关重要。一个在大规模点云数据集上进行预训练的机器人在部署后会遇到未见过的三维物体类别。因此,机器人应能够在真实世界场景...