Meta-Learning with Network Pruning

• URL: http://arxiv.org/abs/2007.03219v2
• Date: Wed, 22 Jul 2020 14:15:19 GMT
• Title: Meta-Learning with Network Pruning
• Authors: Hongduan Tian, Bo Liu, Xiao-Tong Yuan, Qingshan Liu
• Abstract summary: We propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network. We have implemented our approach on top of Reptile assembled with two network pruning routines: Dense-Sparse-Dense (DSD) and Iterative Hard Thresholding (IHT)
• Score: 40.07436648243748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks. To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network. A uniform concentration analysis reveals the benefit of network capacity constraint for reducing generalization gap of the proposed meta-learner. We have implemented our approach on top of Reptile assembled with two network pruning routines: Dense-Sparse-Dense (DSD) and Iterative Hard Thresholding (IHT). Extensive experimental results on benchmark datasets with different over-parameterized deep networks demonstrate that our method not only effectively alleviates meta-overfitting but also in many cases improves the overall generalization performance when applied to few-shot classification tasks.

Related papers

• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• Learning to Learn with Indispensable Connections [6.040904021861969]
  We propose a novel meta-learning method called Meta-LTH that includes indispensible (necessary) connections. Our method improves the classification accuracy by approximately 2% (20-way 1-shot task setting) for omniglot dataset.
  arXiv  Detail & Related papers  (2023-04-06T04:53:13Z)
• Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis [93.55896765176414]
  We propose binary sparse convolutional networks called BSC-Net for efficient point cloud analysis. We employ the differentiable search strategies to discover the optimal opsitions for active site matching in the shifted sparse convolution. Our BSC-Net achieves significant improvement upon our srtong baseline and outperforms the state-of-the-art network binarization methods.
  arXiv  Detail & Related papers  (2023-03-27T13:47:06Z)
• Meta-Learning with Self-Improving Momentum Target [72.98879709228981]
  We propose Self-improving Momentum Target (SiMT) to improve the performance of a meta-learner. SiMT generates the target model by adapting from the temporal ensemble of the meta-learner. We show that SiMT brings a significant performance gain when combined with a wide range of meta-learning methods.
  arXiv  Detail & Related papers  (2022-10-11T06:45:15Z)
• LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification [0.0]
  Recent developments in deep semi-supervised classification have reached unprecedented performance. We propose a new framework, LaplaceNet, for deep semi-supervised classification that has a greatly reduced model complexity. Our model outperforms state-of-the-art methods for deep semi-supervised classification, over several benchmark datasets.
  arXiv  Detail & Related papers  (2021-06-08T17:09:28Z)
• Local Critic Training for Model-Parallel Learning of Deep Neural Networks [94.69202357137452]
  We propose a novel model-parallel learning method, called local critic training. We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We also show that trained networks by the proposed method can be used for structural optimization.
  arXiv  Detail & Related papers  (2021-02-03T09:30:45Z)
• MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning [46.79356071007187]
  We propose a holistic approach to jointly train the backbone network and the channel gating. We develop a federated meta-learning approach to jointly learn good meta-initializations for both backbone networks and gating modules.
  arXiv  Detail & Related papers  (2020-11-25T04:26:23Z)
• QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework [32.465949985191635]
  This paper proposes a meta-based quantizer named QuantNet, which utilizes a differentiable sub-network to directly binarize the full-precision weights. Our method not only solves the problem of gradient mismatching, but also reduces the impact of discretization errors, caused by the binarizing operation in the deployment.
  arXiv  Detail & Related papers  (2020-09-10T01:41:05Z)
• Generalized Reinforcement Meta Learning for Few-Shot Optimization [3.7675996866306845]
  We present a generic and flexible Reinforcement Learning (RL) based meta-learning framework for the problem of few-shot learning. Our framework could be easily extended to do network architecture search.
  arXiv  Detail & Related papers  (2020-05-04T03:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.