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Register a new userLearned Fine-Tuner for Incongruous Few-Shot Adversarial Learning
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Added by
Pu Zhao
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Model-agnostic meta-learning (MAML) effectively meta-learns an initialization of model parameters for few-shot learning where all learning problems share the same format of model parameters -- congruous meta-learning. However, there are few-shot learning scenarios, such as adversarial attack design, where different yet related few-shot learning problems may not share any optimizee variables, necessitating incongruous meta-learning. We extend MAML to this setting -- a Learned Fine Tuner (LFT) is used to replace hand-designed optimizers (such as SGD) for the task-specific fine-tuning. Here, MAML instead meta-learns the parameters of this LFT across incongruous tasks leveraging the learning-to-optimize (L2O) framework such that models fine-tuned with LFT (even from random initializations) adapt quickly to new tasks. As novel contributions, we show that the use of LFT within MAML (i) offers the capability to tackle few-shot learning tasks by meta-learning across incongruous yet related problems and (ii) can efficiently work with first-order and derivative-free few-shot learning problems. Theoretically, we quantify the difference between LFT (for MAML) and L2O. Empirically, we demonstrate the effectiveness of LFT through a novel application of generating universal adversarial attacks across different image sources and sizes in the few-shot learning regime.
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Deep neural networks (DNNs) are known to perform well when deployed to test distributions that shares high similarity with the training distribution. Feeding DNNs with new data sequentially that were unseen in the training distribution has two major challenges -- fast adaptation to new tasks and catastrophic forgetting of old tasks. Such difficulties paved way for the on-going research on few-shot learning and continual learning. To tackle these problems, we introduce Attentive Independent Mechanisms (AIM). We incorporate the idea of learning using fast and slow weights in conjunction with the decoupling of the feature extraction and higher-order conceptual learning of a DNN. AIM is designed for higher-order conceptual learning, modeled by a mixture of experts that compete to learn independent concepts to solve a new task. AIM is a modular component that can be inserted into existing deep learning frameworks. We demonstrate its capability for few-shot learning by adding it to SIB and trained on MiniImageNet and CIFAR-FS, showing significant improvement. AIM is also applied to ANML and OML trained on Omniglot, CIFAR-100 and MiniImageNet to demonstrate its capability in continual learning. Code made publicly available at https://github.com/huang50213/AIM-Fewshot-Continual.
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