but now it is difficult to again make a big stride inaccuracydue to the limitation of only few-shot incremental samples. Inspired by distinctive human cognition ability in life learning, in this work, we propose a novel Big-model drivenFew-shotContinual Learning (B-FSCL...
6.1 Incremental Few-Shot Learning 增量小样本学习 6.2 Continual Meta-Learning 持续元学习 7 FORGETTING IN GENERATIVE MODEL 生成模型中的遗忘 7.1 GAN Training is a Continual Learning Problem GAN 训练是一个持续学习的问题 7.2 Lifelong Learning of Generative Models 生成模型的终身学习 8 FORGETTING IN REINFOR...
Continual learningMeta-learningActive learningFew-shot learningText classificationContinual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in continual learning are mostly confined to a supervised learning setting, ...
内容提示: Continual Training of Language Models for Few-Shot LearningZixuan Ke 1 , Haowei Lin 2 , Yijia Shao 2 , Hu Xu 1 , Lei Shu 1∗ and Bing Liu 11 Department of Computer Science, University of Illinois at Chicago2 Wangxuan Institute of Computer Technology, Peking University1 {zke...
Few-shot Continual Infomax Learning Ziqi Gu#, Chunyan Xu#, Jian Yang, Zhen Cui∗ PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, ...
We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen classes. We approach this problem using the recently published HyperTransformer (HT), a Transformer-based hypernetwork that...
It takes care of creating task sets for our few-shot learning model training and evaluation :param args: Arguments in the form of a Bunch object. Includes all hyperparameters necessary for the data-provider. For transparency and readability reasons to explicitly set as self.object_name all ...
class_change_interval=1) test_data = FewShotLearningDatasetParallel(dataset_name='omniglot_dataset', indexes_of_folders_indicating_class=[-3, -2], train_val_test_split=[0.73982737361, 0.13008631319, 0.13008631319], labels_as_int=False, transforms=transforms, num_classes_per_set=5, num_support_...
研讨会主要关注Continual Learning的话题去接收文章,并且录用的文章会加入CVPR 2020 workshop的文集,已经在CVPR 2020正会投稿的文章也可以提交到我们的workshop,主要方向包括 Lifelong learning Few-shot learning Transfer learning Bio-inspired...
Few-shot continual learning (FSCL) can continuously learn and summarize fault knowledge from limited samples. These traditional FSCL models, when learning from limited samples of new types of bearing faults, exhibit model overfitting and catastrophic forgetting issues after self-updating (new model). ...