知识蒸馏（KD）是压缩边缘设备深层分类模型的有效工具。但是，KD的表现受教师和学生网络之间较大容量差距的影响。最近的方法已诉诸KD的多个教师助手（TA）设置，该设置依次降低了教师模型的大小，以相对弥合这些模型之间的尺寸差距。本文提出了一种称为“知识蒸馏”课程专家选择的新技术，以有效地增强在容量差距问题下对紧凑型学生的学习。该技术建立在以下假设的基础上：学生网络应逐渐使用分层的教学课程来逐步指导，因为它可以从较低（较高的）容量教师网络中更好地学习（硬）数据样本。具体而言，我们的方法是一种基于TA的逐渐的KD技术，它每个输入图像选择单个教师，该课程是基于通过对图像进行分类的难度驱动的课程的。在这项工作中，我们凭经验验证了我们的假设，并对CIFAR-10，CIFAR-100，CINIC-10和Imagenet数据集进行了严格的实验，并在类似VGG的模型，Resnets和WideresNets架构上显示出提高的准确性。

机器学习中的知识蒸馏是将知识从名为教师的大型模型转移到一个名为“学生”的较小模型的过程。知识蒸馏是将大型网络（教师）压缩到较小网络（学生）的技术之一，该网络可以部署在手机等小型设备中。当教师和学生之间的网络规模差距增加时，学生网络的表现就会下降。为了解决这个问题，在教师模型和名为助教模型的学生模型之间采用了中间模型，这反过来弥补了教师与学生之间的差距。在这项研究中，我们已经表明，使用多个助教模型，可以进一步改进学生模型（较小的模型）。我们使用加权集合学习将这些多个助教模型组合在一起，我们使用了差异评估优化算法来生成权重值。

Knowledge distillation (KD) has gained a lot of attention in the field of model compression for edge devices thanks to its effectiveness in compressing large powerful networks into smaller lower-capacity models. Online distillation, in which both the teacher and the student are learning collaboratively, has also gained much interest due to its ability to improve on the performance of the networks involved. The Kullback-Leibler (KL) divergence ensures the proper knowledge transfer between the teacher and student. However, most online KD techniques present some bottlenecks under the network capacity gap. By cooperatively and simultaneously training, the models the KL distance becomes incapable of properly minimizing the teacher's and student's distributions. Alongside accuracy, critical edge device applications are in need of well-calibrated compact networks. Confidence calibration provides a sensible way of getting trustworthy predictions. We propose BD-KD: Balancing of Divergences for online Knowledge Distillation. We show that adaptively balancing between the reverse and forward divergences shifts the focus of the training strategy to the compact student network without limiting the teacher network's learning process. We demonstrate that, by performing this balancing design at the level of the student distillation loss, we improve upon both performance accuracy and calibration of the compact student network. We conducted extensive experiments using a variety of network architectures and show improvements on multiple datasets including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet. We illustrate the effectiveness of our approach through comprehensive comparisons and ablations with current state-of-the-art online and offline KD techniques.

无数据知识蒸馏（DFKD）的目的是在没有培训数据的情况下培训从教师网络的轻量级学生网络。现有方法主要遵循生成信息样本的范式，并通过针对数据先验，边界样本或内存样本来逐步更新学生模型。但是，以前的DFKD方法很难在不同的训练阶段动态调整生成策略，这反过来又很难实现高效且稳定的训练。在本文中，我们探讨了如何从课程学习（CL）的角度来教学学生，并提出一种新方法，即“ CUDFKD”，即“使用课程的无数据知识蒸馏”。它逐渐从简单的样本到困难的样本学习，这类似于人类学习的方式。此外，我们还提供了对主要化最小化（MM）算法的理论分析，并解释了CUDFKD的收敛性。在基准数据集上进行的实验表明，使用简单的课程设计策略，CUDFKD可以在最先进的DFKD方法和不同的基准测试中实现最佳性能，例如CIFAR10上RESNET18模型的95.28 \％TOP1的精度，这是更好的而不是从头开始培训数据。训练很快，在30个时期内达到90 \％的最高精度，并且训练期间的差异稳定。同样在本文中，还分析和讨论了CUDFKD的适用性。

Most existing distillation methods ignore the flexible role of the temperature in the loss function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In general, the temperature controls the discrepancy between two distributions and can faithfully determine the difficulty level of the distillation task. Keeping a constant temperature, i.e., a fixed level of task difficulty, is usually sub-optimal for a growing student during its progressive learning stages. In this paper, we propose a simple curriculum-based technique, termed Curriculum Temperature for Knowledge Distillation (CTKD), which controls the task difficulty level during the student's learning career through a dynamic and learnable temperature. Specifically, following an easy-to-hard curriculum, we gradually increase the distillation loss w.r.t. the temperature, leading to increased distillation difficulty in an adversarial manner. As an easy-to-use plug-in technique, CTKD can be seamlessly integrated into existing knowledge distillation frameworks and brings general improvements at a negligible additional computation cost. Extensive experiments on CIFAR-100, ImageNet-2012, and MS-COCO demonstrate the effectiveness of our method. Our code is available at https://github.com/zhengli97/CTKD.

最初引入了知识蒸馏，以利用来自单一教师模型的额外监督为学生模型培训。为了提高学生表现，最近的一些变体试图利用多个教师利用不同的知识来源。然而，现有研究主要通过对多种教师预测的平均或将它们与其他无标签策略相结合，将知识集成在多种来源中，可能在可能存在低质量的教师预测存在中误导学生。为了解决这个问题，我们提出了信心感知的多教师知识蒸馏（CA-MKD），该知识蒸馏（CA-MKD）在地面真理标签的帮助下，适用于每个教师预测的样本明智的可靠性，与那些接近单热的教师预测标签分配了大量的重量。此外，CA-MKD包含中间层，以进一步提高学生表现。广泛的实验表明，我们的CA-MKD始终如一地优于各种教师学生架构的所有最先进的方法。

One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, conventional distillation approaches overlook this fact and use the same hint points as in the early studies. Therefore, we propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. Our method is applicable for any student network, once it is applied on a chosen teacher network. The proposed approach is validated in CIFAR-100 and ImageNet datasets, using various teacher-student pairs and numerous hint distillation methods. Our results show that hint points selected by our algorithm results in superior compression performance compared to state-of-the-art knowledge distillation algorithms on the same student models and datasets.

Figure 1. An illustration of standard knowledge distillation. Despite widespread use, an understanding of when the student can learn from the teacher is missing.

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.

Knowledge distillation is a widely applicable techniquefor training a student neural network under the guidance of a trained teacher network. For example, in neural network compression, a high-capacity teacher is distilled to train a compact student; in privileged learning, a teacher trained with privileged data is distilled to train a student without access to that data. The distillation loss determines how a teacher's knowledge is captured and transferred to the student. In this paper, we propose a new form of knowledge distillation loss that is inspired by the observation that semantically similar inputs tend to elicit similar activation patterns in a trained network. Similarity-preserving knowledge distillation guides the training of a student network such that input pairs that produce similar (dissimilar) activations in the teacher network produce similar (dissimilar) activations in the student network. In contrast to previous distillation methods, the student is not required to mimic the representation space of the teacher, but rather to preserve the pairwise similarities in its own representation space. Experiments on three public datasets demonstrate the potential of our approach.

知识蒸馏是通过知识转移模型压缩的有效稳定的方法。传统知识蒸馏（KD）是将来自大型和训练有素的教师网络的知识转移到小型学生网络，这是一种单向过程。最近，已经提出了深度相互学习（DML）来帮助学生网络协同和同时学习。然而，据我们所知，KD和DML从未在统一的框架中共同探索，以解决知识蒸馏问题。在本文中，我们调查教师模型在KD中支持更值得信赖的监督信号，而学生则在DML中捕获教师的类似行为。基于这些观察，我们首先建议将KD与DML联合在统一的框架中。此外，我们提出了一个半球知识蒸馏（SOKD）方法，有效提高了学生和教师的表现。在这种方法中，我们在DML中介绍了同伴教学培训时尚，以缓解学生的模仿困难，并利用KD训练有素的教师提供的监督信号。此外，我们还显示我们的框架可以轻松扩展到基于功能的蒸馏方法。在CiFAR-100和Imagenet数据集上的广泛实验证明了所提出的方法实现了最先进的性能。

While depth tends to improve network performances, it also makes gradient-based training more difficult since deeper networks tend to be more non-linear. The recently proposed knowledge distillation approach is aimed at obtaining small and fast-to-execute models, and it has shown that a student network could imitate the soft output of a larger teacher network or ensemble of networks. In this paper, we extend this idea to allow the training of a student that is deeper and thinner than the teacher, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student. Because the student intermediate hidden layer will generally be smaller than the teacher's intermediate hidden layer, additional parameters are introduced to map the student hidden layer to the prediction of the teacher hidden layer. This allows one to train deeper students that can generalize better or run faster, a trade-off that is controlled by the chosen student capacity. For example, on CIFAR-10, a deep student network with almost 10.4 times less parameters outperforms a larger, state-of-the-art teacher network.

除了使用硬标签的标准监督学习外，通常在许多监督学习设置中使用辅助损失来改善模型的概括。例如，知识蒸馏增加了第二个教师模仿模型训练的损失，在该培训中，教师可能是一个验证的模型，可以输出比标签更丰富的分布。同样，在标记数据有限的设置中，弱标记信息以标签函数的形式使用。此处引入辅助损失来对抗标签函数，这些功能可能是基于嘈杂的规则的真实标签近似值。我们解决了学习以原则性方式结合这些损失的问题。我们介绍AMAL，该AMAL使用元学习在验证度量上学习实例特定的权重，以实现损失的最佳混合。在许多知识蒸馏和规则降解域中进行的实验表明，Amal在这些领域中对竞争基准的增长可显着。我们通过经验分析我们的方法，并分享有关其提供性能提升的机制的见解。

Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation.

Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience and usability of BCI applications. However, loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. To address this issue, we introduce knowledge distillation (KD), a learning mechanism developed for transferring knowledge/information between neural network models, to enhance the performance of low-density EEG decoding. Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that encourages a low-density EEG student model to acquire the inter-sample similarity as in a pre-trained teacher model trained on high-density EEG data. The experimental results validate that our SK-KD framework consistently improves motor-imagery EEG decoding accuracy when number of electrodes deceases for the input EEG data. For both common low-density headphone-like and headband-like montages, our method outperforms state-of-the-art KD methods across various EEG decoding model architectures. As the first KD scheme developed for enhancing EEG decoding, we foresee the proposed SK-KD framework to facilitate the practicality of low-density EEG-based BCI in real-world applications.

随着AI芯片（例如GPU，TPU和NPU）的改进以及物联网（IOT）的快速发展，一些强大的深神经网络（DNN）通常由数百万甚至数亿个参数组成，这些参数是可能不适合直接部署在低计算和低容量单元（例如边缘设备）上。最近，知识蒸馏（KD）被认为是模型压缩的有效方法之一，以减少模型参数。 KD的主要概念是从大型模型（即教师模型）的特征图中提取有用的信息，以引用成功训练一个小型模型（即学生模型），该模型大小比老师小得多。尽管已经提出了许多基于KD的方法来利用教师模型中中间层的特征图中的信息，但是，它们中的大多数并未考虑教师模型和学生模型之间的特征图的相似性，这可能让学生模型学习无用的信息。受到注意机制的启发，我们提出了一种新颖的KD方法，称为代表教师钥匙（RTK），该方法不仅考虑了特征地图的相似性，而且还会过滤掉无用的信息以提高目标学生模型的性能。在实验中，我们使用多个骨干网络（例如Resnet和wideresnet）和数据集（例如CIFAR10，CIFAR100，SVHN和CINIC10）验证了我们提出的方法。结果表明，我们提出的RTK可以有效地提高基于注意的KD方法的分类精度。

在多种方式知识蒸馏研究的背景下，现有方法主要集中在唯一的学习教师最终产出问题。因此，教师网络与学生网络之间存在深处。有必要强制学生网络来学习教师网络的模态关系信息。为了有效利用从教师转移到学生的知识，采用了一种新的模型关系蒸馏范式，通过建模不同的模态之间的关系信息，即学习教师模级克矩阵。

知识蒸馏已成为获得紧凑又有效模型的重要方法。为实现这一目标，培训小型学生模型以利用大型训练有素的教师模型的知识。然而，由于教师和学生之间的能力差距，学生的表现很难达到老师的水平。关于这个问题，现有方法建议通过代理方式减少教师知识的难度。我们认为这些基于代理的方法忽视了教师的知识损失，这可能导致学生遇到容量瓶颈。在本文中，我们从新的角度来缓解能力差距问题，以避免知识损失的目的。我们建议通过对抗性协作学习建立一个更有力的学生，而不是牺牲教师的知识。为此，我们进一步提出了一种逆势协作知识蒸馏（ACKD）方法，有效提高了知识蒸馏的性能。具体来说，我们用多个辅助学习者构建学生模型。同时，我们设计了对抗的对抗性协作模块（ACM），引入注意机制和对抗的学习，以提高学生的能力。四个分类任务的广泛实验显示了拟议的Ackd的优越性。

Knowledge Distillation (KD) has been extensively used for natural language understanding (NLU) tasks to improve a small model's (a student) generalization by transferring the knowledge from a larger model (a teacher). Although KD methods achieve state-of-the-art performance in numerous settings, they suffer from several problems limiting their performance. It is shown in the literature that the capacity gap between the teacher and the student networks can make KD ineffective. Additionally, existing KD techniques do not mitigate the noise in the teacher's output: modeling the noisy behaviour of the teacher can distract the student from learning more useful features. We propose a new KD method that addresses these problems and facilitates the training compared to previous techniques. Inspired by continuation optimization, we design a training procedure that optimizes the highly non-convex KD objective by starting with the smoothed version of this objective and making it more complex as the training proceeds. Our method (Continuation-KD) achieves state-of-the-art performance across various compact architectures on NLU (GLUE benchmark) and computer vision tasks (CIFAR-10 and CIFAR-100).

知识蒸馏将知识从繁琐的老师转移到小学生。最近的结果表明，对学生友好的老师更适合提炼，因为它提供了更可转移的知识。在这项工作中，我们提出了新颖的框架“修剪，然后蒸馏”，该框架首先修剪模型，以使其更具转让，然后将其提炼为学生。我们提供了几个探索性示例，这些探索性示例与原始未经修复的网络相比，教师教的更好。从理论上讲，我们进一步表明，修剪的老师在蒸馏中扮演正规剂的角色，从而减少了概括误差。基于此结果，我们提出了一种新型的神经网络压缩方案，该方案根据修剪教师形成学生网络，然后采用“修剪，然后蒸馏”策略。该代码可在https://github.com/ososos8888/prune-then-distill上找到