298 research outputs found
Dynamic Multi-Arm Bandit Game Based Multi-Agents Spectrum Sharing Strategy Design
For a wireless avionics communication system, a Multi-arm bandit game is
mathematically formulated, which includes channel states, strategies, and
rewards. The simple case includes only two agents sharing the spectrum which is
fully studied in terms of maximizing the cumulative reward over a finite time
horizon. An Upper Confidence Bound (UCB) algorithm is used to achieve the
optimal solutions for the stochastic Multi-Arm Bandit (MAB) problem. Also, the
MAB problem can also be solved from the Markov game framework perspective.
Meanwhile, Thompson Sampling (TS) is also used as benchmark to evaluate the
proposed approach performance. Numerical results are also provided regarding
minimizing the expectation of the regret and choosing the best parameter for
the upper confidence bound
CR-SFP: Learning Consistent Representation for Soft Filter Pruning
Soft filter pruning~(SFP) has emerged as an effective pruning technique for
allowing pruned filters to update and the opportunity for them to regrow to the
network. However, this pruning strategy applies training and pruning in an
alternative manner, which inevitably causes inconsistent representations
between the reconstructed network~(R-NN) at the training and the pruned
network~(P-NN) at the inference, resulting in performance degradation. In this
paper, we propose to mitigate this gap by learning consistent representation
for soft filter pruning, dubbed as CR-SFP. Specifically, for each training
step, CR-SFP optimizes the R-NN and P-NN simultaneously with different
distorted versions of the same training data, while forcing them to be
consistent by minimizing their posterior distribution via the bidirectional
KL-divergence loss. Meanwhile, the R-NN and P-NN share backbone parameters thus
only additional classifier parameters are introduced. After training, we can
export the P-NN for inference. CR-SFP is a simple yet effective training
framework to improve the accuracy of P-NN without introducing any additional
inference cost. It can also be combined with a variety of pruning criteria and
loss functions. Extensive experiments demonstrate our CR-SFP achieves
consistent improvements across various CNN architectures. Notably, on ImageNet,
our CR-SFP reduces more than 41.8\% FLOPs on ResNet18 with 69.2\% top-1
accuracy, improving SFP by 2.1\% under the same training settings. The code
will be publicly available on GitHub.Comment: 11 pages, 4 figure
AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction
Structured pruning methods are developed to bridge the gap between the
massive scale of neural networks and the limited hardware resources. Most
current structured pruning methods rely on training datasets to fine-tune the
compressed model, resulting in high computational burdens and being
inapplicable for scenarios with stringent requirements on privacy and security.
As an alternative, some data-free methods have been proposed, however, these
methods often require handcraft parameter tuning and can only achieve
inflexible reconstruction. In this paper, we propose the Automatic Data-Free
Pruning (AutoDFP) method that achieves automatic pruning and reconstruction
without fine-tuning. Our approach is based on the assumption that the loss of
information can be partially compensated by retaining focused information from
similar channels. Specifically, We formulate data-free pruning as an
optimization problem, which can be effectively addressed through reinforcement
learning. AutoDFP assesses the similarity of channels for each layer and
provides this information to the reinforcement learning agent, guiding the
pruning and reconstruction process of the network. We evaluate AutoDFP with
multiple networks on multiple datasets, achieving impressive compression
results. For instance, on the CIFAR-10 dataset, AutoDFP demonstrates a 2.87\%
reduction in accuracy loss compared to the recently proposed data-free pruning
method DFPC with fewer FLOPs on VGG-16. Furthermore, on the ImageNet dataset,
AutoDFP achieves 43.17\% higher accuracy than the SOTA method with the same
80\% preserved ratio on MobileNet-V1.Comment: 11 pages, 16 figure
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