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...
Domain Incremental Learning (DIL) focuses on handling complex domain shifts of a continuous data stream for visual tasks such as image classification and image segmentation. In real life, severe domain gaps in DIL are generated from various sources such as data style shifts, data quality ...
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 ...
CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning The key challenge of cross-modal domain-incremental learning (DIL) is to enable the learning model to continuously learn from novel data with different fea... Y Feng,Z Tian,Y Zhu,... 被引量: 0发表:...
The best of the proposed approaches achieves a remarkable relative improvement (an average of about 30%) over the best of the state-of-the-art exemplar-free methods for three standard Domain Incremental Learning (DIL) tasks. S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor ...
Automatic machine learning extrapolation component In this component, we train an ML algorithm to perform “black-box” predictions of the target value. This is used to generate synthetic data from the sampled data, in order to cover the input space for the SR task uniformly. The motivation for...
The key challenge of cross-modal domain-incremental learning (DIL) is to enable the learning model to continuously learn from novel data with different fea... Y Feng,Z Tian,Y Zhu,... 被引量: 0发表: 2024年 Deep Balanced Learning for Long-tailed Facial Expressions Recognition The analysis of...
Existing approaches to Domain Incremental Learning (DIL) address catastrophic forgetting by storing and rehearsing exemplars from old domains.However, exemplar-based solutions are not always viable due to data privacy concerns or storage limitations.Therefore, Non-Exemplar Domain Incremental Learning (NEDIL...
Incremental Learning (IL) aims to accumulate knowledge from sequential input tasks while overcoming catastrophic forgetting. Existing IL methods typically assume that an incoming task has only increments of classes or domains, referred to as Class IL (CIL) or Domain IL (DIL), respectively. In this...
Finally, we discuss how to incorporate sentiment lexicons information in neural models (word embedding) for better performance.doi:10.1007/978-3-030-45442-5_81Shamsuddeen Hassan MuhammadPavel BrazdilAlípio JorgeSpringer, Cham