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...
To address this issue, we propose a novel multi-domain adaptation method for object detection based on incremental learning. Specifically, the incremental learning network saves the knowledge of multiple domains and makes the model to fuse the knowledge of different domains during the training ...
图3. The pipeline of Incremental Learning Through Deep Adaption 图4. Controller Module 2. 再看Cross-sensor Cross-sensor本身,既可以是训练集的cross出发,比如我如何设计一个model尽可能的使用多(不同sensor)的数据集,就像本文把这个描述为multi-domain问题——当然这样就增加了数据,也就提升了效果;又可以从结...
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...
However, the deep learning usually requires numerous labelled data for training a good deep network. In the real world, collecting enough labelled ECG data is usually expensive and laborious. Meanwhile, most of the existing automatic ECG diagnosis methods are aimed at heartbeat and single-lead ECG...
Cross-domain unsupervised transfer learning employs a generative adversarial network to transfer labeled images from the source domain to the unlabeled domain [7-9], together with the transfer of discriminative information to train the unsupervised reID model. Though some improvements have been made, ...
First, a multi-branch Dilated Convolution Aggregation Module (DCAM) is proposed as a feature learning structure for the feature augmentation networks. Second, to better capture spatial (location) information and to suppress channel redundancy, we propose a Dilated Convolution and Channel Attention ...
Deep Learning on domain adaptation, transfer and multi-task applications Incremental, online and active transfer for open-ended learning Innovative adaptive procedures with applications e.g. in computer vision or computational biology Domain adaptation theory ...
ECCV-22Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation Prototype continual domain adaptation 基于原型的类增量domain adaptation Federated Semi-Supervised Domain Adaptation via Knowledge Transfer Federated semi-supervised DA 联邦半监督DA ...
While machine learning approaches have improved DST, they have notable limitations. These approaches often overlook unseen slot values during training and use two separate modules to extract, generate, or match slot values, leading to high time and resource consumption. Moreover, learning and deducing...