2 research outputs found
Rewarded meta-pruning: Meta Learning with Rewards for Channel Pruning
Convolutional Neural Networks (CNNs) have a large number of parameters and
take significantly large hardware resources to compute, so edge devices
struggle to run high-level networks. This paper proposes a novel method to
reduce the parameters and FLOPs for computational efficiency in deep learning
models. We introduce accuracy and efficiency coefficients to control the
trade-off between the accuracy of the network and its computing efficiency. The
proposed Rewarded meta-pruning algorithm trains a network to generate weights
for a pruned model chosen based on the approximate parameters of the final
model by controlling the interactions using a reward function. The reward
function allows more control over the metrics of the final pruned model.
Extensive experiments demonstrate superior performances of the proposed method
over the state-of-the-art methods in pruning ResNet-50, MobileNetV1, and
MobileNetV2 networks
EvolveNet: Evolving Networks by Learning Scale of Depth and Width
Convolutional neural networks (CNNs) have shown decent performance in a variety of computer vision tasks. However, these network configurations are largely hand-crafted, which leads to inefficiency in the constructed network. Various other algorithms have been proposed to address this issue, but the inefficiencies resulting from human intervention have not been addressed. Our proposed EvolveNet algorithm is a task-agnostic evolutionary search algorithm that can find optimal depth and width scales automatically in an efficient way. The optimal configurations are not found using grid search, and are instead evolved from an existing network. This eliminates inefficiencies that emanate from hand-crafting, thus reducing the drop in accuracy. The proposed algorithm is a framework to search through a large search space of subnetworks until a suitable configuration is found. Extensive experiments on the ImageNet dataset demonstrate the superiority of the proposed method by outperforming the state-of-the-art methods