Improved Knowledge Distillation via Teacher Assistant (1902.03393v2)

Abstract: Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too large to be deployed on edge devices like smartphones or embedded sensor nodes. There have been efforts to compress these networks, and a popular method is knowledge distillation, where a large (teacher) pre-trained network is used to train a smaller (student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation, which employs an intermediate-sized network (teacher assistant) to bridge the gap between the student and the teacher. Moreover, we study the effect of teacher assistant size and extend the framework to multi-step distillation. Theoretical analysis and extensive experiments on CIFAR-10,100 and ImageNet datasets and on CNN and ResNet architectures substantiate the effectiveness of our proposed approach.

Improved Knowledge Distillation via Teacher Assistant

The paper "Improved Knowledge Distillation via Teacher Assistant" by Seyed Iman Mirzadeh et al. addresses a significant challenge in the field of knowledge distillation for neural networks: the performance degradation of student networks when there is a substantial size gap between the teacher and student networks. The authors introduce a novel multi-step knowledge distillation framework that employs intermediate-sized networks, termed teacher assistants (TAs), to effectively bridge this gap.

Key Contributions and Findings

1. Size Gap Analysis: The paper identifies that when the gap in the number of parameters between the teacher and student networks is large, the efficacy of knowledge distillation diminishes. This observation is counterintuitive since one would expect a stronger, larger teacher to produce better outcomes for the student. However, empirical results on CIFAR-10, CIFAR-100, and ImageNet datasets show that an oversized teacher can lead to inferior performance in the student network.
2. Teacher Assistant Knowledge Distillation (TAKD):
   • Multi-Step Distillation: TAKD introduces intermediate TAs to facilitate a smoother knowledge transfer between the teacher and student networks. This approach mitigates the adverse effects of large capacity differences by providing incremental learning stages through intermediate networks.
   • TA Size Effectiveness: Experiments demonstrate that TAs, regardless of their exact size, consistently improve the performance of the student network compared to conventional one-step distillation (BLKD). The paper found that employing TAs with capacities averaged around the performance yields of the teacher and student results in optimal knowledge transfer.
3. Theoretical Justification: The paper provides theoretical insights into why TAKD works better than BLKD. By leveraging the principles of VC theory, the authors argue that the TA networks reduce the approximation error and the rate of learning complexity, which typically hampers the direct distillation approach.
4. Empirical Evaluation: The authors perform extensive empirical evaluations across different architectures (plain CNN and ResNet) and datasets (CIFAR-10, CIFAR-100, and ImageNet). They show robust improvements over both BLKD and training from scratch (NOKD). For instance, in CIFAR-100, the TAKD approach using a student size of 2 and teacher size of 10 shows that TA models with 6 or 4 convolutional layers serve as effective intermediaries, highlighting the advantage of the multi-step distillation process.
5. Implementation and Practicality: The paper also discusses the computational aspects of TAKD. By presenting a dynamic programming solution to determine the best sequence of TAs given fixed computing resources or time constraints, the authors provide a practical pathway for adopting this method in real-world scenarios.

Implications and Future Directions

The implications of this research are multifaceted, touching both theoretical and practical aspects of AI and model compression:

• Enhanced AI Deployment: The ability to compress large neural networks effectively opens up broader applicability for deploying sophisticated AI models on edge devices with limited computational resources, such as smartphones and IoT devices.
• Theoretical Insights: The findings provide a stronger understanding of the mechanics behind knowledge distillation, prompting further theoretical studies on the best strategies for model compression and learning transfer.
• Optimizing Distillation Paths: Future research could explore more sophisticated methods to automate the selection of optimal TA sequences, ensuring that knowledge distillation is both efficient and effective under various constraints.

In conclusion, the "Improved Knowledge Distillation via Teacher Assistant" paper presents a significant advancement in the field of neural network compression and knowledge transfer. By introducing intermediate teacher assistants, the authors not only improve the performance outcomes of student networks but also pave the way for more efficient and scalable deployment of deep learning models in resource-constrained environments.