Vision Transformer (ViT) extracts the final representation from either class token or an average of all patch tokens, following the architecture of Transformer in Natural Language Processing (NLP) or Convolutional Neural Networks (CNNs) in computer vision. However, studies for the best way of aggregating the patch tokens are still limited to average pooling, while widely-used pooling strategies, such as max and GeM pooling, can be considered. Despite their effectiveness, the existing pooling strategies do not consider the architecture of ViT and the channel-wise difference in the activation maps, aggregating the crucial and trivial channels with the same importance. In this paper, we present Group Generalized Mean (GGeM) pooling as a simple yet powerful pooling strategy for ViT. GGeM divides the channels into groups and computes GeM pooling with a shared pooling parameter per group. As ViT groups the channels via a multi-head attention mechanism, grouping the channels by GGeM leads to lower head-wise dependence while amplifying important channels on the activation maps. Exploiting GGeM shows 0.1%p to 0.7%p performance boosts compared to the baselines and achieves state-of-the-art performance for ViT-Base and ViT-Large models in ImageNet-1K classification task. Moreover, GGeM outperforms the existing pooling strategies on image retrieval and multi-modal representation learning tasks, demonstrating the superiority of GGeM for a variety of tasks. GGeM is a simple algorithm in that only a few lines of code are necessary for implementation.

深度神经网络的合奏表现出了卓越的性能，但是它们的沉重计算成本阻碍将它们应用于资源有限的环境。它激发了从合奏老师的知识到较小的学生网络，并且有两个重要的设计选择，用于这种合奏蒸馏：1）如何构建学生网络，以及2）在培训期间应显示哪些数据。在本文中，我们提出了一种平均水平技术，其中有多个子网的学生经过培训以吸收合奏教师的功能多样性，但是这些子网的适当平均进行推理，提供了一个学生网络，没有额外的推理成本。我们还提出了一种扰动策略，该策略寻求投入，从中可以更好地转移到学生的教师中。结合这两个，我们的方法在以前的各种图像分类任务上的方法上有了显着改进。

最近，学到的图像压缩方法优于传统手工制作的方法，包括BPG。该成功的关键之一是学习的熵模型，该模型估计了量化潜在表示的概率分布。与其他视觉任务一样，最近学习的熵模型基于卷积神经网络（CNN）。但是，CNN由于局部连接性的性质而在建模长期依赖性方面有限制，这在图像压缩中可能是一个重要的瓶颈，其中降低空间冗余是一个关键点。为了克服这个问题，我们提出了一个名为Informand Transformer（Informer）的新型熵模型，该模型使用注意机制以内容依赖性方式利用全球和局部信息。我们的实验表明，告密者可以提高利率 - 对柯达和Tecnick数据集的最先进方法的延伸性能，而没有二次计算复杂性问题。我们的源代码可在https://github.com/naver-ai/informer上获得。

类别不平衡数据的问题在于，由于少数类别的数据缺乏数据，分类器的泛化性能劣化。在本文中，我们提出了一种新的少数民族过度采样方法，通过利用大多数类作为背景图像的丰富背景来增加多元化的少数民族样本。为了使少数民族样本多样化，我们的主要思想是将前景补丁从少数级别粘贴到来自具有富裕环境的多数类的背景图像。我们的方法很简单，可以轻松地与现有的长尾识别方法结合。我们通过广泛的实验和消融研究证明了提出的过采样方法的有效性。如果没有任何架构更改或复杂的算法，我们的方法在各种长尾分类基准上实现了最先进的性能。我们的代码将在链接上公开提供。

变形金刚正在改变计算机视觉的景观，特别是对于识别任务。检测变压器是对象检测的第一个完全结束的学习系统，而视觉变压器是用于图像分类的第一个完全变压器的架构。在本文中，我们集成了视觉和检测变压器（Vidt）以构建有效和高效的物体探测器。 VIDT引入了重新配置的注意模块，将最近的Swin变压器扩展为独立对象检测器，然后是计算高效的变压器解码器，该解码器利用多尺度特征和辅助技术来提高检测性能，而无需多大增加计算负载。 Microsoft Coco基准数据集上的广泛评估结果表明，VIDT在现有的基于变压器的对象检测器中获得了最佳的AP和延迟折衷，并且由于大型型号的高可扩展性而实现了49.2AP。我们将在https://github.com/naver-ai/vidt发布代码和培训的型号

Vision Transformer (ViT) extends the application range of transformers from language processing to computer vision tasks as being an alternative architecture against the existing convolutional neural networks (CNN). Since the transformer-based architecture has been innovative for computer vision modeling, the design convention towards an effective architecture has been less studied yet. From the successful design principles of CNN, we investigate the role of spatial dimension conversion and its effectiveness on transformer-based architecture. We particularly attend to the dimension reduction principle of CNNs; as the depth increases, a conventional CNN increases channel dimension and decreases spatial dimensions. We empirically show that such a spatial dimension reduction is beneficial to a transformer architecture as well, and propose a novel Pooling-based Vision Transformer (PiT) upon the original ViT model. We show that PiT achieves the improved model capability and generalization performance against ViT. Throughout the extensive experiments, we further show PiT outperforms the baseline on several tasks such as image classification, object detection, and robustness evaluation. Source codes and ImageNet models are available at https://github.com/naver-ai/pit.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular explanations methods have been found to be fragile to minor perturbations of input features or model parameters. Relying on constraint relaxation techniques from non-convex optimization, we develop a method that upper-bounds the largest change an adversary can make to a gradient-based explanation via bounded manipulation of either the input features or model parameters. By propagating a compact input or parameter set as symbolic intervals through the forwards and backwards computations of the neural network we can formally certify the robustness of gradient-based explanations. Our bounds are differentiable, hence we can incorporate provable explanation robustness into neural network training. Empirically, our method surpasses the robustness provided by previous heuristic approaches. We find that our training method is the only method able to learn neural networks with certificates of explanation robustness across all six datasets tested.

Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and utilization to amortize their steep cost is a challenging task requiring careful balance of compute, memory, and network resources. Moreover, a plethora of each model's tuning knobs drastically affect the performance, with optimal values often depending on the underlying cluster's characteristics, which necessitates a complex cluster-workload co-design process. To facilitate the design space exploration of such massive DL training clusters, we introduce COMET a holistic cluster design methodology and workflow to jointly study the impact of parallelization strategies and key cluster resource provisioning on the performance of distributed DL training. We develop a step-by-step process to establish a reusable and flexible methodology, and demonstrate its application with a case study of training a Transformer-1T model on a cluster of variable compute, memory, and network resources. Our case study demonstrates COMET's utility in identifying promising architectural optimization directions and guiding system designers in configuring key model and cluster parameters.

Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining \textit{robust} feature attributions. However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods. In this paper, we provide new insights by taking such normalization into account. First, we show that for every non-negative homogeneous neural network, a naive $\ell_2$-robust criterion for gradients is \textit{not} normalization invariant, which means that two functions with the same normalized gradient can have different values. Second, we formulate a normalization invariant cosine distance-based criterion and derive its upper bound, which gives insight for why simply minimizing the Hessian norm at the input, as has been done in previous work, is not sufficient for attaining robust feature attribution. Finally, we propose to combine both $\ell_2$ and cosine distance-based criteria as regularization terms to leverage the advantages of both in aligning the local gradient. As a result, we experimentally show that models trained with our method produce much more robust interpretations on CIFAR-10 and ImageNet-100 without significantly hurting the accuracy, compared to the recent baselines. To the best of our knowledge, this is the first work to verify the robustness of interpretation on a larger-scale dataset beyond CIFAR-10, thanks to the computational efficiency of our method.