用风格学习:跨任务和领域的持续语义细分
概括
本研究介绍了学习与风格 (LwS),这是一个持续学习的新框架,它解决了图像理解中的领域和任务转移. LwS有效地将知识泛化到各个领域和任务,防止灾难性的遗忘.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 深度学习模型因任务和域变化而难以理解现实世界的图像.
- 现有的方法分别解决域调整和类增量学习,使它们的统一解决方案成为一个开放的问题.
- 在联合任务和领域转移下,持续的语义细分是一个重大挑战.
研究的目的:
- 开发一个统一的持续学习框架,同时解决领域和任务转移.
- 在持续学习场景中,解决输入和标签空间的语义转变.
- 提出一种在不断变化的环境中对灾难性遗忘具有坚固的方法.
主要方法:
- 在任务和领域转移下正式引入持续学习.
- 利用风格转移技术在增量学习过程中扩展跨领域的知识.
- 采用强大的蒸框架,在增量域移动下保留任务知识.
- 介绍学习与风格 (LwS) 框架.
主要成果:
- LwS框架展示了在所有遇到的领域中逐步获得的任务知识的概括能力.
- LwS证明了对灾难性遗忘的坚强,这是持续学习中常见的问题.
- 对自动驾驶数据集的广泛实验表明,LwS的性能优于现有的方法.
结论:
- 拟议的LwS框架提供了一个强大的解决方案,用于在联合任务和领域转移下持续的语义细分.
- 现有的方法不足以应对动态,多领域环境中持续学习的复杂性.
- 在深度学习中,LwS通过提供对域和任务可变性的统一方法来推进该领域.
相关概念视频
01:12Perceptual Constancy
385
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
385
02:07Improving Translational Accuracy
10.2K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.2K
01:29Aggregates Classification
317
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
317