Affiliations: University of Oxford, Meta AI Research 本文属于表示学习 (representation learning) 领域,我们知道,对于深度学习模型而言鲁棒性是非常重要的,因为我们可以用一个很小但是精心选择的噪声来对某个 input 进行扰动,从而极大地影响神经网络的输出,甚至导致其被错误分类,为了解决这个问题,常用的办法是在网络训...
Deep learning models that require vast amounts of training data struggle to achieve good animal sound classification (ASC) performance. Among recent few-shot ASC methods to address the data shortage problem regarding animals that are difficult to observe, model-agnostic meta-learning (MAML)...
To investigate these hypotheses, we introduce a replay-based recurrent reinforcement learning (3RL) methodology for task-agnostic CL agents. We assess 3RL on a synthetic task and the Meta-World benchmark, which includes 50 unique manipulation tasks. Our results demonstrate that 3RL outperforms ...
[33] employed an extension of the Model Agnostic Meta-Learning (MAML) algorithm to adapt well to various handwritten styles. However, it falls short of addressing general feature extraction in handwriting. Others, like Coquenet et al. [34], proposed a network using a vertical attention ...
[20] introduced image deformation and meta-learning to enhance one-shot learning. Murty et al. [53] diversified and augmented training data to improve performance in low-resource scenarios. Rajendran et al. [54] employed data augmentation (MetaAug) to generate more diverse data for robust ...
etc. In the early stages of computer vision research, the main focus was to build algorithms to detect edges, curves, corners, and other basic shapes. Before the era ofdeep learning, image processing relied on gray level segmentation and this approach wasn’t robust enough to represent complex...
Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, pages 1126–1135, 2017. [4] Kun Fu, Tengfei Zhang, Yue Zhang, Menglong Yan, Zhonghan Chang, Zhengyuan Zhang, and Xian Sun. Meta-ssd: Towards fast adaptation for few-shot ...
Schuster S, Gupta S, Shah R, et al. Cross-lingual transfer learning for multilingual task oriented dialog. ArXiv: 1810.13327 Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning-...
[23] performed the protein pre-training task on large amounts of unlabelled data to obtain the robust protein encoding model with enhanced structural information of amino acid sequences, and then fine-tuned the encoding model on the decoding process, i.e., the DTA prediction modeling process, ...
Model-agnostic meta- learning for fast adaptation of deep networks. arXiv preprint arXiv:1703.03400, 2017. Gehrmann, S., Deng, Y., and Rush, A. M. Bottom-up abstractive summarization. arXiv preprint arXiv:1808.10792, 2018. Gillick, D., Brunk, C., Vinyals, O., and Subramanya, A. ...