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}。

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

在线知识蒸馏（OKD）通过相互利用教师和学生之间的差异来改善所涉及的模型。它们之间的差距上有几个关键的瓶颈 - 例如，为什么以及何时以及何时损害表现，尤其是对学生的表现？如何量化教师和学生之间的差距？ - 接受了有限的正式研究。在本文中，我们提出了可切换的在线知识蒸馏（Switokd），以回答这些问题。 Switokd的核心思想不是专注于测试阶段的准确性差距，而是通过两种模式之间的切换策略来适应训练阶段的差距，即蒸馏差距 - 专家模式（暂停老师，同时暂停教师保持学生学习）和学习模式（重新启动老师）。为了拥有适当的蒸馏差距，我们进一步设计了一个自适应开关阈值，该阈值提供了有关何时切换到学习模式或专家模式的正式标准，从而改善了学生的表现。同时，老师从我们的自适应切换阈值中受益，并基本上与其他在线艺术保持同步。我们进一步将Switokd扩展到具有两个基础拓扑的多个网络。最后，广泛的实验和分析验证了Switokd在最新面前的分类的优点。我们的代码可在https://github.com/hfutqian/switokd上找到。

知识蒸馏（KD）已广泛发展并增强了各种任务。经典的KD方法将KD损失添加到原始的跨熵（CE）损失中。我们尝试分解KD损失，以探索其与CE损失的关系。令人惊讶的是，我们发现它可以被视为CE损失和额外损失的组合，其形式与CE损失相同。但是，我们注意到额外的损失迫使学生学习教师绝对概率的相对可能性。此外，这两个概率的总和是不同的，因此很难优化。为了解决这个问题，我们修改了配方并提出分布式损失。此外，我们将教师的目标输出作为软目标，提出软损失。结合软损失和分布式损失，我们提出了新的KD损失（NKD）。此外，我们将学生的目标输出稳定，将其视为无需教师的培训的软目标，并提出了无教师的新KD损失（TF-NKD）。我们的方法在CIFAR-100和Imagenet上实现了最先进的性能。例如，以Resnet-34为老师，我们将Imagenet TOP-1的RESNET18的TOP-1精度从69.90％提高到71.96％。在没有教师的培训中，Mobilenet，Resnet-18和Swintransformer-tiny的培训占70.04％，70.76％和81.48％，分别比基线高0.83％，0.86％和0.30％。该代码可在https://github.com/yzd-v/cls_kd上找到。

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 (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.

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

基于蒸馏的压缩网络的性能受蒸馏质量的管辖。大型网络（教师）到较小网络（学生）的次优蒸馏的原因主要归因于给定教师与学生对的学习能力中的差距。虽然很难蒸馏所有教师的知识，但可以在很大程度上控制蒸馏质量以实现更好的性能。我们的实验表明，蒸馏品质主要受教师响应的质量来限制，这反过来又受到其反应中存在相似信息的影响。训练有素的大容量老师在学习细粒度辨别性质的过程中丢失了类别之间的相似性信息。没有相似性信息导致蒸馏过程从一个例子 - 许多阶级学习减少到一个示例 - 一类学习，从而限制了教师的不同知识的流程。由于隐式假设只能蒸馏出灌输所知，而不是仅关注知识蒸馏过程，我们仔细审查了知识序列过程。我们认为，对于给定的教师 - 学生对，通过在训练老师的同时找到批量大小和时代数量之间的甜蜜点，可以提高蒸馏品。我们讨论了找到这种甜蜜点以便更好地蒸馏的步骤。我们还提出了蒸馏假设，以区分知识蒸馏和正则化效果之间的蒸馏过程的行为。我们在三个不同的数据集中进行我们的所有实验。

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。

自我介绍在训练过程中利用自身的非均匀软监管，并在没有任何运行时成本的情况下提高性能。但是，在训练过程中的开销经常被忽略，但是在巨型模型的时代，培训期间的时间和记忆开销越来越重要。本文提出了一种名为ZIPF标签平滑（ZIPF的LS）的有效自我验证方法，该方法使用网络的直立预测来生成软监管，该软监管在不使用任何对比样本或辅助参数的情况下符合ZIPF分布。我们的想法来自经验观察，即当对网络进行适当训练时，在按样品的大小和平均分类后，应遵循分布的分布，让人联想到ZIPF的自然语言频率统计信息，这是在按样品中的大小和平均值进行排序之后进行的。 。通过在样本级别和整个培训期内强制执行此属性，我们发现预测准确性可以大大提高。使用INAT21细粒分类数据集上的RESNET50，与香草基线相比，我们的技术获得了 +3.61％的准确性增长，而与先前的标签平滑或自我验证策略相比，增益增加了0.88％。该实现可在https://github.com/megvii-research/zipfls上公开获得。

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。

Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-art approaches, this paper presents a method dubbed DIST to distill better from a stronger teacher. We empirically find that the discrepancy of predictions between the student and a stronger teacher may tend to be fairly severer. As a result, the exact match of predictions in KL divergence would disturb the training and make existing methods perform poorly. In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly. Besides, considering that different instances have different semantic similarities to each class, we also extend this relational match to the intra-class level. Our method is simple yet practical, and extensive experiments demonstrate that it adapts well to various architectures, model sizes and training strategies, and can achieve state-of-the-art performance consistently on image classification, object detection, and semantic segmentation tasks. Code is available at: https://github.com/hunto/DIST_KD .

最先进的蒸馏方法主要基于中间层的深层特征，而logit蒸馏的重要性被极大地忽略了。为了提供研究逻辑蒸馏的新观点，我们将经典的KD损失重新分为两个部分，即目标类知识蒸馏（TCKD）和非目标类知识蒸馏（NCKD）。我们凭经验研究并证明了这两个部分的影响：TCKD转移有关训练样本“难度”的知识，而NCKD是Logit蒸馏起作用的重要原因。更重要的是，我们揭示了经典的KD损失是一种耦合的配方，该配方抑制了NCKD的有效性，并且（2）限制了平衡这两个部分的灵活性。为了解决这些问题，我们提出了脱钩的知识蒸馏（DKD），使TCKD和NCKD能够更有效，更灵活地发挥其角色。与基于功能的复杂方法相比，我们的DKD可相当甚至更好的结果，并且在CIFAR-100，ImageNet和MS-Coco数据集上具有更好的培训效率，用于图像分类和对象检测任务。本文证明了Logit蒸馏的巨大潜力，我们希望它对未来的研究有所帮助。该代码可从https://github.com/megvii-research/mdistiller获得。

知识蒸馏是一种培训小型学生网络的流行技术，以模仿更大的教师模型，例如网络的集合。我们表明，虽然知识蒸馏可以改善学生泛化，但它通常不得如此普遍地工作：虽然在教师和学生的预测分布之间，甚至在学生容量的情况下，通常仍然存在令人惊讶的差异完美地匹配老师。我们认为优化的困难是为什么学生无法与老师匹配的关键原因。我们还展示了用于蒸馏的数据集的细节如何在学生与老师匹配的紧密关系中发挥作用 - 以及教师矛盾的教师并不总是导致更好的学生泛化。

知识蒸馏是将“知识”从大型模型（教师）转移到更紧凑的（学生）的过程，通常在模型压缩的背景下使用。当两个模型都具有相同的体系结构时，此过程称为自distillation。几项轶事表明，一个自灭的学生可以在持有的数据上胜过老师的表现。在这项工作中，我们系统地研究了许多设置。我们首先表明，即使有一个高度准确的老师，自我介绍也使学生在所有情况下都可以超越老师。其次，我们重新审视了（自我）蒸馏的现有理论解释，并确定矛盾的例子，揭示了这些解释的可能缺点。最后，我们通过损失景观几何形状的镜头为自我鉴定的动态提供了另一种解释。我们进行了广泛的实验，以表明自我验证会导致最小化的最小值，从而导致更好的概括。

The generalization and learning speed of a multi-class neural network can often be significantly improved by using soft targets that are a weighted average of the hard targets and the uniform distribution over labels. Smoothing the labels in this way prevents the network from becoming over-confident and label smoothing has been used in many state-of-the-art models, including image classification, language translation and speech recognition. Despite its widespread use, label smoothing is still poorly understood. Here we show empirically that in addition to improving generalization, label smoothing improves model calibration which can significantly improve beam-search. However, we also observe that if a teacher network is trained with label smoothing, knowledge distillation into a student network is much less effective. To explain these observations, we visualize how label smoothing changes the representations learned by the penultimate layer of the network. We show that label smoothing encourages the representations of training examples from the same class to group in tight clusters. This results in loss of information in the logits about resemblances between instances of different classes, which is necessary for distillation, but does not hurt generalization or calibration of the model's predictions.

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.

最近对知识蒸馏的研究发现，组合来自多位教师或学生的“黑暗知识”是有助于为培训创造更好的软目标，但以更大的计算和/或参数的成本为本。在这项工作中，我们通过在同一批量中传播和集合其他样本的知识来提供批处理知识合奏（烘焙）以生产用于锚固图像的精细柔软目标。具体地，对于每个感兴趣的样本，根据采样间的亲和力加权知识的传播，其与当前网络一起估计。然后可以集合传播的知识以形成更好的蒸馏靶。通过这种方式，我们的烘焙框架只通过单个网络跨多个样本进行在线知识。与现有知识合并方法相比，它需要最小的计算和内存开销。广泛的实验表明，轻质但有效的烘烤始终如一地提升多个数据集上各种架构的分类性能，例如，在想象网上的显着+ 0.7％的VINE-T的增益，只有+ 1.5％计算开销和零附加参数。烘焙不仅改善了Vanilla基线，还超越了所有基准的单一网络最先进。

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.

Mixup is a popular data augmentation technique based on creating new samples by linear interpolation between two given data samples, to improve both the generalization and robustness of the trained model. Knowledge distillation (KD), on the other hand, is widely used for model compression and transfer learning, which involves using a larger network's implicit knowledge to guide the learning of a smaller network. At first glance, these two techniques seem very different, however, we found that ``smoothness" is the connecting link between the two and is also a crucial attribute in understanding KD's interplay with mixup. Although many mixup variants and distillation methods have been proposed, much remains to be understood regarding the role of a mixup in knowledge distillation. In this paper, we present a detailed empirical study on various important dimensions of compatibility between mixup and knowledge distillation. We also scrutinize the behavior of the networks trained with a mixup in the light of knowledge distillation through extensive analysis, visualizations, and comprehensive experiments on image classification. Finally, based on our findings, we suggest improved strategies to guide the student network to enhance its effectiveness. Additionally, the findings of this study provide insightful suggestions to researchers and practitioners that commonly use techniques from KD. Our code is available at https://github.com/hchoi71/MIX-KD.