Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
|Place of Conferral||中国科学院自动化研究所|
|Keyword||元学习 持续学习 知识迁移 灾难遗忘 生成式对抗网络|
In non-stationary environments, artificial learning systems need to face the challenge of continuously learning new tasks. However, there exist limitations while using traditional deep learning paradigm to learn new tasks in non-stationary environments. On the one hand, the deep learning models are prone to overfitting when the training data of a new task is scarce. On the other hand, after learning a new task, the deep learning models will catastrophically forget how to solve previous tasks. In contrast, humans are capable of continually learning new tasks without forgetting old ones in non-stationary environments. Furthermore, we can also obtain transferable prior knowledge from learning different tasks which can help to learn new tasks rapidly using only a few examples. Therefore, to overcome these limitations in deep learning models and build more intelligent artificial learning systems, our research concentrates on two aspects. The first is how to make deep learning models obtain transferable prior knowledge to help to learn new tasks, and the second is how to make deep learning models capable of continually learning new tasks. Our contributions are as follows:
A novel meta learning method for few-shot learning tasks. The main purpose of this research is to learn the transferable prior knowledge for rapidly learning and adapting to new few-shot tasks. This approach contains two key components, e.g. TargetNet module and MetaNet module. The TargetNet is a classification network that is designed for few-shot learning, and we use directly use the architecture of matching networks. The MetaNet can directly produce functional weights for TargetNet using the training samples of a few-shot task. In the MetaNet, we have designed a task context encoder and a conditional weight generator. The task context encoder aims to encode all training samples of a task and generate a task context feature. The conditional weight generator can generate the functional weights of TargetNet using the task context feature. Through episodic training procedure, MetaNet can learn the transferable prior knowledge. We also propose an intertask normalization strategy to utilize the common information shared among tasks during training. After training, for an unseen few-shot task, MetaNet can directly use the training samples to produce the functional weights of TargetNet for solving this task. No further fine-tuning is required, hence the goal of fast learning and adaptation is achieved. We have conducted several experiments on synthetic datasets to show the intuition of our approach, through visualizing the decision boundaries of TargetNet for different few-shot tasks. The experiment results on two few-shot learning benchmarks show the validity of our approach, and we have achieved the state-of-the-art performance on the one-shot learning task.
An approach of knowledge transfer for facial attribute transformation. The main purpose of this research is to learn transferable prior knowledge of facial attribute transformation. It can transform different attributes of an unseen facial image without changing the personal identity. In this approach, we design a conditional recycle generative adversarial network, which consists of a conditional generator and a discriminator with auxiliary classifier. During training, we first input the original facial image and target facial attributes to the generator and produce a transformed facial image. Then, we recycle the transformed facial image and original facial attributes to the generator and produce a facial image that should be completely the same as the original one. In order to maintain the facial personal identity and keep recycle consistency, we propose a recycle-consistent loss. To guide the generator to produce the facial image with specified attributes, we add a multi-label classification loss in the discriminator. During training, we only use the facial attribute labels. We can regard each facial image attribute transformation as a task. Through learning a lot of facial image attribute transformation tasks, the generator could learn the transferable prior knowledge of facial attribute transformation which can be applied to new facial images. In several experiments on a facial image dataset, we qualitatively compare and evaluate the results which prove the validity of our approach.
A novel rehearsal-based class incremental learning approach. Inspired by complementary learning systems theory, we propose to utilize different types of memory in artificial neural networks to alleviate the catastrophic forgetting problem during continual learning. On the one hand, we propose an incremental concept learning network which is a remolded classification network with a dynamic external memory module. During learning new classes, it can dynamically increase the corresponding number of concept vectors in the concept memory matrix. To alleviate the catastrophic forgetting problem, we propose a concept contrastive loss that can reduce the intra-class feature variance during continual learning. On the other hand, we propose the concept memory recall network which is a conditional generative adversarial network, to consolidate learned concepts and recall old concepts during learning new categories. We also propose an online balanced recall strategy to mitigate the catastrophic forgetting problem. To show the intuition of our approach, we visualize the feature variation of different learned classes during continual learning. We conduct class incremental learning experiments on several datasets to show the validity of our approach. Comparing with rehearsal-based continual learning methods, we have achieved state-of-the-art performance.
A continual learning approach based on feature null space gradient conversion. The neural network model, which is optimized by stochastic gradient descent algorithms, is prone to happen catastrophic forgetting during continual learning. The main reason lies in that the feature mapping relation of old task data in the neural network is heavily changed after learning new task data. Therefore, if we can adjust the new loss gradients into the directs that lead to the change of feature mapping relation as small as possible while learning new task data, the catastrophic forgetting problem can be mitigated. Hence，we propose the feature null space gradient conversion method. In each linear layer, we should project the gradient from new task loss into the null space of the space spanned by old task data feature vectors. In this way, the old feature mapping relation in the linear layer will not be changed during learning new task data. Furthermore, the old task data feature mapping relation in the whole network will be minimally changed and the catastrophic forgetting will be alleviated. In order to improve the computational efficiency of feature null space gradient conversion function, we propose an online accumulative way to approximate the null space projector as well as three different batch feature matrix construction strategies。To exhibit the intuition, we conduct the experiments on a synthetic dataset and visualize the variation of decision boundaries of neural network classifier during incrementally learn new classes. It is worth noting that our approach does not require any historical data during continual learning. Comparing with other regularization based approaches in the same condition, our results obviously outperform. In several exploration experiments, we find that our approach could control the stability-plasticity balance of the neural network during continual learning through adjust a hyperparameter, which means some biological explanations.
|李怀宇. 面向非平稳环境的知识迁移方法研究[D]. 中国科学院自动化研究所. 中国科学院大学,2020.|
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