Jointly Training and Pruning Convolutional Neural Networks Using Reinforcement Learning with Learnable Agent Guidance and Alignment

Core Concepts

This paper proposes a novel structural pruning approach that jointly learns the weights and architectures of convolutional neural network (CNN) models using a reinforcement learning (RL) agent. The agent's actions determine the pruning ratios of the CNN's layers, and the resulting model's accuracy serves as its reward. The authors address the challenge of the dynamic reward function by designing a mechanism to model the changing dynamics of the reward function and provide a representation of it to the RL agent.

Abstract

The paper proposes a novel method for jointly training and pruning convolutional neural network (CNN) models using reinforcement learning (RL). The key elements are:

RL Agent: The agent's actions determine the pruning ratios of the CNN's layers, and the resulting model's accuracy serves as its reward.

Modeling Dynamic Reward Function: As the model's weights are updated during training, the reward function for the RL agent becomes dynamic. The authors address this by using a recurrent model to provide a representation of the changing state of the environment to the agent.

Soft Regularization: Since the model's weights and pruning cannot be trained simultaneously, the authors use a soft regularization term to align the model's weights with the sub-network selected by the agent.

The authors conduct experiments on CIFAR-10 and ImageNet using ResNet and MobileNet models. The results demonstrate the effectiveness of the proposed method in finding efficient yet accurate pruned models, outperforming various baseline pruning techniques, especially those using RL.

Stats

The proposed method can prune ResNet-56 on CIFAR-10 to 50% FLOPs while improving the accuracy by 0.45%.
For pruning ResNet-18 on ImageNet, the method achieves 0.80% higher ∆-Acc Top-1 compared to the baseline.
When pruning ResNet-34 on ImageNet, the method has 1% higher ∆-Acc Top-1 and 0.46% higher ∆-Acc Top-5 than the GP method, while pruning a similar FLOPs budget.
For MobileNet-V2 on ImageNet, the method obtains 0.5% higher ∆-Acc Top-1 compared to AMC while pruning 0.6% lower FLOPs.

Quotes

"We propose a novel channel pruning method that jointly learns the weights and prunes the architecture of a CNN model using an RL agent."
"We design a mechanism to model the dynamics of our evolving pruning environment. To do so, we use a recurrent model that provides a representation of the state of the environment to the agent."
"We regularize the model's weights to align with the selected sub-network by the agent. By doing so, our pruned model can readily recover its high performance in fine-tuning."

Key Insights Distilled From

Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment

by Alireza Ganj... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19490.pdf

Deeper Inquiries

How can the proposed method be extended to handle more complex neural network architectures beyond CNNs, such as transformers or graph neural networks

The proposed method can be extended to handle more complex neural network architectures beyond CNNs by adapting the reinforcement learning framework to suit the specific characteristics of transformers or graph neural networks. For transformers, which are commonly used in natural language processing tasks, the agent's actions could involve pruning attention heads or layers instead of channels. The state representation would need to capture the structure of the transformer model, including the attention mechanisms and positional encodings. The reward function could be based on the performance of the pruned transformer model on language modeling or translation tasks.
Similarly, for graph neural networks (GNNs), the agent's actions could involve pruning nodes or edges based on their importance in the graph structure. The state representation would need to include information about the graph topology, node features, and edge connections. The reward function could be based on the accuracy of the pruned GNN model in graph classification or node classification tasks.
Adapting the method for transformers or GNNs would require careful consideration of the unique architectural elements and training objectives of these models, but the fundamental framework of jointly training and pruning using reinforcement learning could be applied with appropriate modifications.

What are the potential limitations of the soft regularization approach used to align the model's weights with the selected sub-network, and how could it be further improved

The soft regularization approach used to align the model's weights with the selected sub-network may have limitations in cases where the regularization strength is not appropriately balanced. If the regularization term is too strong, it could overly constrain the model's weights, leading to underfitting and reduced performance. On the other hand, if the regularization term is too weak, it may not effectively guide the model to align with the selected sub-network, resulting in suboptimal pruning.
To improve the soft regularization approach, one potential strategy is to dynamically adjust the regularization strength during training based on the model's performance. This adaptive regularization technique could involve monitoring the model's accuracy during training and modulating the regularization term accordingly. Additionally, exploring different regularization functions or incorporating additional constraints based on the structure of the neural network could enhance the alignment between the model's weights and the pruned architecture.

Could the proposed mechanism for modeling the dynamic reward function be applied to other reinforcement learning problems with non-stationary environments, beyond the specific task of model pruning

The proposed mechanism for modeling the dynamic reward function could be applied to other reinforcement learning problems with non-stationary environments by generalizing the concept of representing the changing dynamics of the environment. In tasks where the reward function evolves over time or is influenced by external factors, a similar approach of using a recurrent model to capture the state of the environment could be employed.
For example, in dynamic control tasks where the reward landscape changes based on the system's state or external conditions, the recurrent model could encode the temporal dependencies and provide a representation of the evolving environment to the agent. By augmenting the agent's observations with this representation, the agent can adapt its policy to the changing reward function and make informed decisions in non-stationary environments.
By extending the proposed mechanism to other reinforcement learning problems, researchers can address the challenge of training agents in dynamic and evolving environments, leading to more robust and adaptive learning systems.