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.

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

Knowledge distillation aims at transferring knowledge acquired in one model (a teacher) to another model (a student) that is typically smaller. Previous approaches can be expressed as a form of training the student to mimic output activations of individual data examples represented by the teacher. We introduce a novel approach, dubbed relational knowledge distillation (RKD), that transfers mutual relations of data examples instead. For concrete realizations of RKD, we propose distance-wise and angle-wise distillation losses that penalize structural differences in relations. Experiments conducted on different tasks show that the proposed method improves educated student models with a significant margin. In particular for metric learning, it allows students to outperform their teachers' performance, achieving the state of the arts on standard benchmark datasets.

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.

Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcing it to mimic the attention maps of a powerful teacher network.To that end, we propose several novel methods of transferring attention, showing consistent improvement across a variety of datasets and convolutional neural network architectures. Code and models for our experiments are available at https://github.com/szagoruyko/attention-transfer.

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。

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

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.

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。

Knowledge Distillation (KD) consists of transferring "knowledge" from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness, without sacrificing too much performance. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating the effect of the teacher outputs on both predicted and nonpredicted classes.

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。

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.

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。

We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN maps from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model; (2) the student DNN outperforms the original DNN; and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch.

尽管知识蒸馏有经验成功，但仍然缺乏理论基础，可以自然地导致计算廉价的实现。为了解决这一问题，我们使用最近提出的熵函数来促进信息理论与知识蒸馏之间的替代联系。在这样做时，我们介绍了两个不同的互补损失，旨在最大限度地提高学生和教师陈述之间的相关性和互信。我们的方法对知识蒸馏和跨模型转移任务的最先进的竞争性能实现了最先进的，同时产生明显较低的培训开销，而不是密切相关和类似的方法。我们进一步展示了我们对二元蒸馏任务的方法的有效性，由此，我们将光线光到新的最先进的二进制量化。代码，评估协议和培训的型号将公开可用。

深度学习的巨大成功主要是由于大规模的网络架构和高质量的培训数据。但是，在具有有限的内存和成像能力的便携式设备上部署最近的深层模型仍然挑战。一些现有的作品通过知识蒸馏进行了压缩模型。不幸的是，这些方法不能处理具有缩小图像质量的图像，例如低分辨率（LR）图像。为此，我们采取了开创性的努力，从高分辨率（HR）图像到达将处理LR图像的紧凑型网络模型中学习的繁重网络模型中蒸馏有用的知识，从而推动了新颖的像素蒸馏的当前知识蒸馏技术。为实现这一目标，我们提出了一名教师助理 - 学生（TAS）框架，将知识蒸馏分解为模型压缩阶段和高分辨率表示转移阶段。通过装备新颖的特点超分辨率（FSR）模块，我们的方法可以学习轻量级网络模型，可以实现与重型教师模型相似的准确性，但参数更少，推理速度和较低分辨率的输入。在三个广泛使用的基准，\即，幼崽200-2011，Pascal VOC 2007和ImageNetsub上的综合实验证明了我们方法的有效性。

Transferring knowledge from a teacher neural network pretrained on the same or a similar task to a student neural network can significantly improve the performance of the student neural network. Existing knowledge transfer approaches match the activations or the corresponding handcrafted features of the teacher and the student networks. We propose an information-theoretic framework for knowledge transfer which formulates knowledge transfer as maximizing the mutual information between the teacher and the student networks. We compare our method with existing knowledge transfer methods on both knowledge distillation and transfer learning tasks and show that our method consistently outperforms existing methods. We further demonstrate the strength of our method on knowledge transfer across heterogeneous network architectures by transferring knowledge from a convolutional neural network (CNN) to a multi-layer perceptron (MLP) on CIFAR-10. The resulting MLP significantly outperforms the-state-of-the-art methods and it achieves similar performance to the CNN with a single convolutional layer. * Contributed during an internship at Amazon.

尽管深层神经网络在各种任务中取得了巨大的成功，但它们不断增加的规模也为部署带来了重要的开销。为了压缩这些模型，提出了知识蒸馏将知识从笨拙（教师）网络转移到轻量级（学生）网络中。但是，老师的指导并不总是改善学生的概括，尤其是当学生和老师之间的差距很大时。以前的作品认为，这是由于老师的高确定性，导致更难适应的标签。为了软化这些标签，我们提出了一种修剪方法，称为预测不确定性扩大（PRUE），以简化教师。具体而言，我们的方法旨在减少教师对数据的确定性，从而为学生产生软预测。我们从经验上研究了提出的方法通过在CIFAR-10/100，Tiny-Imagenet和Imagenet上实验的实验的有效性。结果表明，接受稀疏教师培训的学生网络取得更好的表现。此外，我们的方法允许研究人员从更深的网络中提取知识，以进一步改善学生。我们的代码公开：\ url {https://github.com/wangshaopu/prue}。

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.

知识蒸馏通常涉及如何有效地定义和转移知识从教师到学生。尽管最近的自我监督的对比知识取得了最佳表现，但迫使网络学习此类知识可能会损害对原始班级识别任务的表示。因此，我们采用替代性的自我监督的增强任务来指导网络学习原始识别任务和自我监督的辅助任务的共同分布。它被证明是一种更丰富的知识，可以提高表示能力而不会失去正常的分类能力。此外，以前的方法仅在最终层之间传递概率知识是不完整的。我们建议将几个辅助分类器附加到层次中间特征图中，以生成多样化的自我监督知识，并执行一对一的转移以彻底教授学生网络。我们的方法显着超过了先前的SOTA SSKD，CIFAR-100的平均改善为2.56 \％，并且在广泛使用的网络对上的Imagenet上有0.77 \％的提高。代码可在https://github.com/winycg/hsakd上找到。