Channel Distillation: Channel-Wise Attention for Knowledge Distillation (2006.01683v1)

Abstract: Knowledge distillation is to transfer the knowledge from the data learned by the teacher network to the student network, so that the student has the advantage of less parameters and less calculations, and the accuracy is close to the teacher. In this paper, we propose a new distillation method, which contains two transfer distillation strategies and a loss decay strategy. The first transfer strategy is based on channel-wise attention, called Channel Distillation (CD). CD transfers the channel information from the teacher to the student. The second is Guided Knowledge Distillation (GKD). Unlike Knowledge Distillation (KD), which allows the student to mimic each sample's prediction distribution of the teacher, GKD only enables the student to mimic the correct output of the teacher. The last part is Early Decay Teacher (EDT). During the training process, we gradually decay the weight of the distillation loss. The purpose is to enable the student to gradually control the optimization rather than the teacher. Our proposed method is evaluated on ImageNet and CIFAR100. On ImageNet, we achieve 27.68% of top-1 error with ResNet18, which outperforms state-of-the-art methods. On CIFAR100, we achieve surprising result that the student outperforms the teacher. Code is available at https://github.com/zhouzaida/channel-distillation.

• The paper introduces the Channel Distillation (CD) approach that uses channel-wise attention to effectively transfer crucial feature information from teacher to student networks.
• The Guided Knowledge Distillation (GKD) mechanism selectively transfers correct predictions, reducing error propagation during the training process.
• The Early Decay Teacher (EDT) strategy gradually diminishes the teacher's influence, allowing the student network to optimize independently and boost performance on benchmark datasets.

Channel Distillation: A Technical Analysis

The paper "Channel Distillation: Channel-Wise Attention for Knowledge Distillation" introduces a novel approach to knowledge distillation, which is pivotal for enhancing the efficiency of models in computationally constrained environments. This method emphasizes channel-wise attention, termed Channel Distillation (CD), to refine the knowledge transfer process between teacher and student networks.

Core Contributions

1. Channel-Wise Distillation (CD): Building on the concept of channel-wise attention from SENet, CD facilitates the transfer of channel-specific attentional information from a teacher to a student network. By focusing on channels as distinct information carriers, the student network mimics the teacher's ability to prioritize essential visual patterns, thus improving feature extraction.
2. Guided Knowledge Distillation (GKD): Unlike traditional Knowledge Distillation that aligns the full prediction distribution, GKD selectively transfers only correctly predicted outputs from the teacher. This reduces the propagation of errors, aligning student learning with accurate teacher-derived patterns.
3. Early Decay Teacher (EDT): This strategy modulates the distillation loss during training. As the student's learning progresses, the influence of the teacher's supervision gradually wanes, thereby allowing the student to explore its optimization path independently.

Experimental Validation

The proposed methods have been rigorously tested on popular datasets like ImageNet and CIFAR100. Notably, the model with CD, GKD, and EDT outperforms previous state-of-the-art methods on these datasets. For instance, on CIFAR100, the student network notably surpasses its teacher, demonstrating the efficacy of the proposed distillation approach. On ImageNet, the ResNet18 student network achieves a top-1 error rate of 27.61%, indicating a significant improvement over baseline knowledge distillation techniques such as KD, FitNets, and RKD.

Implications and Future Directions

This work offers substantial implications for the domain of model compression and efficient inference on resource-limited devices. The channel-focused attention approach underscores a shift towards more granular, attention-based mechanisms in knowledge distillation, which could be extended into other modalities beyond vision, such as natural language processing or speech recognition.

The scalability of CD, along with GKD and EDT, invites further exploration in tandem with emerging neural architectures, ensuring compatibility and performance gains across diverse application scenarios. Additionally, the adaptability of the distillation process, by selectively diminishing teacher influence, might inspire novel self-supervised learning frameworks that dynamically balance external guidance with intrinsic model capabilities.

The paper charts a definitive course for the refinement of knowledge distillation practices by integrating attention mechanisms at a systemic level, thus paving the way for robust, scalable models that maintain high accuracy with reduced computational overhead.