正规化和转移学习是两种流行的技术，可以增强看不见数据的概念，这是机器学习的根本问题。正则化技术是多功能的，因为它们是任务和架构 - 不可知论，但它们不会利用大量数据。传输学习方法学会从一个域转移到另一个域的知识，但可能无法跨解任务和架构拓展，并且可能会引入适应目标任务的新培训成本。为了弥合两者之间的差距，我们提出了一种可转移的扰动，Metaperturb，这是荟萃学会，以提高看不见数据的泛化性能。 Metaperturb实现为基于集的轻量级网络，该网络是不可知的，其尺寸和输入的顺序，它们在整个层上共享。然后，我们提出了一个元学习框架，共同训练了与异构任务相同的扰动功能。正如Metaperturb在层次和任务的不同分布上训练的集合函数，它可以概括为异构任务和架构。通过将不同的神经架构应用于各种规范和微调，验证对特定源域和架构的Metaperturb培训的疗效和普遍性，验证了特定的源域和架构的疗效和普遍性。结果表明，Metaperturb培训的网络显着优于大多数任务和架构的基线，参数大小的忽略不计，并且没有封闭曲调。

共享初始化参数的元学习已显示在解决少量学习任务方面非常有效。然而，将框架扩展到许多射击场景，这可能进一步提高其实用性，这一切相对忽略了由于内梯度步长的长链中的元学习的技术困难。在本文中，我们首先表明允许元学习者采取更多的内梯度步骤更好地捕获异构和大规模任务分布的结构，从而导致获得更好的初始化点。此外，为了增加元更新的频率，即使是过度长的内部优化轨迹，我们建议估计关于初始化参数的改变的任务特定参数的所需移位。通过这样做，我们可以随意增加元更新的频率，从而大大提高了元级收敛以及学习初始化的质量。我们验证了我们在异构的大规模任务集中验证了方法，并表明该算法在泛型性能和收敛方面以及多任务学习和微调基线方面主要优于先前的一阶元学习方法。 。

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights for each task. In addition, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum for MTL. We conduct experiments using (5-class, 1-shot) and (5-class, 5shot) recognition tasks on two challenging few-shot learning benchmarks: miniImageNet and Fewshot-CIFAR100. Extensive comparisons to related works validate that our meta-transfer learning approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy 1 .Optimize θ by Eq. 3; 5 end 6 Optimize Φ S {1,2} and θ by Eq. 4 and Eq. 5; 7 while not done do 8 Sample class-k in T (te) ; 9 Compute Acc k for T (te) ; 10 end 11 Return class-m with the lowest accuracy Acc m .

基于元学习的现有方法通过从（源域）基础类别的培训任务中学到的元知识来预测（目标域）测试任务的新颖类标签。但是，由于范围内可能存在较大的域差异，大多数现有作品可能无法推广到新颖的类别。为了解决这个问题，我们提出了一种新颖的对抗特征增强（AFA）方法，以弥合域间隙，以几乎没有学习。该特征增强旨在通过最大化域差异来模拟分布变化。在对抗训练期间，通过将增强特征（看不见的域）与原始域（可见域）区分开来学习域歧视器，而将域差异最小化以获得最佳特征编码器。所提出的方法是一个插件模块，可以轻松地基于元学习的方式将其集成到现有的几种学习方法中。在九个数据集上进行的广泛实验证明了我们方法对跨域几乎没有射击分类的优越性，与最新技术相比。代码可从https://github.com/youthhoo/afa_for_few_shot_learning获得

我们关注模型概括中最坏的情况，因为一个模型旨在在许多看不见的域上表现良好，而只有一个单个域可供训练。我们提出基于元学习的对抗领域的增强，以解决此范围泛化问题。关键思想是利用对抗性训练来创建“虚构的”但“具有挑战性”的人群，模型可以从中学会通过理论保证进行概括。为了促进快速和理想的域增强，我们将模型训练施加在元学习方案中，并使用Wasserstein自动编码器放宽广泛使用的最坏情况的约束。我们通过整合有效域概括的不确定性定量来进一步改善我们的方法。在多个基准数据集上进行的广泛实验表明其在解决单个领域概括方面的出色性能。

传输学习方法旨在使用在丰富的源域上掠过的模型来提高数据稀缺目标域中的性能。一种成本效益的策略，线性探测涉及冻结源模型并培训目标域的新分类头。此策略的表现优于更昂贵但最先进的方法 - 将源模型的所有参数微调到目标域 - 可能是因为微调允许模型从中间层利用有用的信息否则被稍后的净化层丢弃。我们探讨了这些中间层可能直接剥削的假设。我们提出了一种方法，头对脚趾探测（Head2ToE），其从源模型的所有层中选择特征，以训练目标域的分类头。在VTAB-1K的评估中，Head2Toe与平均微调获得的性能相匹配，同时减少培训和储存成本一百倍或更多，但批判性地，用于分配转移，头部2ToE优于微调。

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.

Few-shot image classification consists of two consecutive learning processes: 1) In the meta-learning stage, the model acquires a knowledge base from a set of training classes. 2) During meta-testing, the acquired knowledge is used to recognize unseen classes from very few examples. Inspired by the compositional representation of objects in humans, we train a neural network architecture that explicitly represents objects as a dictionary of shared components and their spatial composition. In particular, during meta-learning, we train a knowledge base that consists of a dictionary of component representations and a dictionary of component activation maps that encode common spatial activation patterns of components. The elements of both dictionaries are shared among the training classes. During meta-testing, the representation of unseen classes is learned using the component representations and the component activation maps from the knowledge base. Finally, an attention mechanism is used to strengthen those components that are most important for each category. We demonstrate the value of our interpretable compositional learning framework for a few-shot classification using miniImageNet, tieredImageNet, CIFAR-FS, and FC100, where we achieve comparable performance.

Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift. We thus propose the Omni-Training framework to seamlessly bridge pre-training and meta-training for data-efficient few-shot learning. Our first contribution is a tri-flow Omni-Net architecture. Besides the joint representation flow, Omni-Net introduces two parallel flows for pre-training and meta-training, responsible for improving domain transferability and task transferability respectively. Omni-Net further coordinates the parallel flows by routing their representations via the joint-flow, enabling knowledge transfer across flows. Our second contribution is the Omni-Loss, which introduces a self-distillation strategy separately on the pre-training and meta-training objectives for boosting knowledge transfer throughout different training stages. Omni-Training is a general framework to accommodate many existing algorithms. Evaluations justify that our single framework consistently and clearly outperforms the individual state-of-the-art methods on both cross-task and cross-domain settings in a variety of classification, regression and reinforcement learning problems.

Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In today's world, the rate of data creation greatly surpasses the rate of data annotation. Full reliance on human annotations is just a temporary means to solve current closed problems in AI. In reality, only a tiny fraction of data is annotated. Annotation Efficient Learning (AEL) is a study of algorithms to train models effectively with fewer annotations. To thrive in AEL environments, we need deep learning techniques that rely less on manual annotations (e.g., image, bounding-box, and per-pixel labels), but learn useful information from unlabeled data. In this thesis, we explore five different techniques for handling AEL.

在本文中，我们询问视觉变形金刚（VIT）是否可以作为改善机器学习模型对抗逃避攻击的对抗性鲁棒性的基础结构。尽管较早的作品集中在改善卷积神经网络上，但我们表明VIT也非常适合对抗训练以实现竞争性能。我们使用自定义的对抗训练配方实现了这一目标，该配方是在Imagenet数据集的一部分上使用严格的消融研究发现的。与卷积相比，VIT的规范培训配方建议强大的数据增强，部分是为了补偿注意力模块的视力归纳偏置。我们表明，该食谱在用于对抗训练时可实现次优性能。相比之下，我们发现省略所有重型数据增强，并添加一些额外的零件（$ \ varepsilon $ -Warmup和更大的重量衰减），从而大大提高了健壮的Vits的性能。我们表明，我们的配方在完整的Imagenet-1k上概括了不同类别的VIT体系结构和大规模模型。此外，调查了模型鲁棒性的原因，我们表明，在使用我们的食谱时，在训练过程中产生强烈的攻击更加容易，这会在测试时提高鲁棒性。最后，我们通过提出一种量化对抗性扰动的语义性质并强调其与模型的鲁棒性的相关性来进一步研究对抗训练的结果。总体而言，我们建议社区应避免将VIT的规范培训食谱转换为在对抗培训的背景下进行强大的培训和重新思考常见的培训选择。

在本文中，我们看看跨域几秒分类的问题，旨在从以前看不见的类别和域名的域中学习分类器。最近的方法广泛地通过参数参数化与前者通常在大型训练集上学习的任务 - 不可行的和任务特定权重参数来解决这个问题，并且后者通过在小型支撑集上通过辅助网络动态预测。在这项工作中，我们专注于对后者的估计，并建议将特定于任务特定权重直接在小型支架上学习，以与动态估计它们。特别地，通过系统分析，我们示出了通过矩阵形式的参数适配器以备体网络的多个中间层的参数化适配器的任务特定权重显着提高了Meta DataSet中最先进模型的性能基准以较小的额外费用。

几乎没有学习方法的目的是训练模型，这些模型可以根据少量数据轻松适应以前看不见的任务。最受欢迎，最优雅的少学习方法之一是模型敏捷的元学习（MAML）。这种方法背后的主要思想是学习元模型的一般权重，该权重进一步适应了少数梯度步骤中的特定问题。但是，该模型的主要限制在于以下事实：更新过程是通过基于梯度的优化实现的。因此，MAML不能总是在一个甚至几个梯度迭代中将权重修改为基本水平。另一方面，使用许多梯度步骤会导致一个复杂且耗时的优化程序，这很难在实践中训练，并且可能导致过度拟合。在本文中，我们提出了HyperMAML，这是MAML的新型概括，其中更新过程的训练也是模型的一部分。也就是说，在HyperMAML中，我们没有使用梯度下降来更新权重，而是为此目的使用可训练的超级净机。因此，在此框架中，该模型可以生成重大更新，其范围不限于固定数量的梯度步骤。实验表明，超型MAML始终胜过MAML，并且在许多标准的几次学习基准测试基准中与其他最先进的技术相当。

使用卷积神经网络（CNN）已经显着改善了几种图像处理任务，例如图像分类和对象检测。与Reset和Abseralnet一样，许多架构在创建时至少在一个数据集中实现了出色的结果。培训的一个关键因素涉及网络的正规化，这可以防止结构过度装备。这项工作分析了在过去几年中开发的几种正规化方法，显示了不同CNN模型的显着改进。该作品分为三个主要区域：第一个称为“数据增强”，其中所有技术都侧重于执行输入数据的更改。第二个，命名为“内部更改”，旨在描述修改神经网络或内核生成的特征映射的过程。最后一个称为“标签”，涉及转换给定输入的标签。这项工作提出了与关于正则化的其他可用调查相比的两个主要差异：（i）第一个涉及在稿件中收集的论文并非超过五年，并第二个区别是关于可重复性，即所有作品此处推荐在公共存储库中可用的代码，或者它们已直接在某些框架中实现，例如Tensorflow或Torch。

元学习在现有基准测试基准上的成功取决于以下假设：元训练任务的分布涵盖了元测试任务。经常违反任务不足或非常狭窄的元训练任务分布的应用中的假设会导致记忆或学习者过度拟合。最近的解决方案已追求元训练任务的增强，而同时产生正确和充分虚构任务的问题仍然是一个悬而未决的问题。在本文中，我们寻求一种方法，该方法是通过任务上采样网络从任务表示从任务表示的映射任务。此外，最终的方法将对抗性任务上采样（ATU）命名为足以生成可以通过最大化对抗性损失来最大程度地贡献最新元学习者的任务。在几乎没有正弦的回归和图像分类数据集上，我们从经验上验证了ATU在元测试性能中的最新任务增强策略的明显改善以及上采样任务的质量。

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections-one between each layer and its subsequent layer-our network has L(L+1) 2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.

Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of objects (e.g. leg as opposed to head of a person), thereby letting the network generalize better and have better object localization capabilities. On the other hand, current methods for regional dropout remove informative pixels on training images by overlaying a patch of either black pixels or random noise. Such removal is not desirable because it leads to information loss and inefficiency during training. We therefore propose the CutMix augmentation strategy: patches are cut and pasted among training images where the ground truth labels are also mixed proportionally to the area of the patches. By making efficient use of training pixels and retaining the regularization effect of regional dropout, CutMix consistently outperforms the state-of-the-art augmentation strategies on CI-FAR and ImageNet classification tasks, as well as on the Im-ageNet weakly-supervised localization task. Moreover, unlike previous augmentation methods, our CutMix-trained ImageNet classifier, when used as a pretrained model, results in consistent performance gains in Pascal detection and MS-COCO image captioning benchmarks. We also show that CutMix improves the model robustness against input corruptions and its out-of-distribution detection performances. Source code and pretrained models are available at https://github.com/clovaai/CutMix-PyTorch.

There is a growing interest in learning data representations that work well for many different types of problems and data. In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits. Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains. Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations. We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to perform well uniformly.

对抗性扰动对于证明深度学习模型的鲁棒性至关重要。通用的对抗扰动（UAP）可以同时攻击多个图像，因此提供了更统一的威胁模型，从而避免了图像攻击算法。但是，当从不同的图像源绘制图像时（例如，具有不同的图像分辨率）时，现有的UAP生成器不发达。在图像来源的真实普遍性方面，我们将UAP生成的新颖看法是一个定制的几个实例，它利用双杆优化和学习优化的（L2O）技术（L2O）技术，以提高攻击成功率（ASR）（ASR） ）。我们首先考虑流行模型不可知的元学习（MAML）框架，以将UAP生成器元素进行。但是，我们看到MAML框架并未直接提供跨图像源的通用攻击，从而要求我们将其与L2O的另一个元学习框架集成在一起。元学习UAP发电机（i）的最终方案的性能（ASR高50％）比预计梯度下降等基线的方案（II）比香草L2O和MAML框架的性能更好（37％）（当适用），（iii）能够同时处理不同受害者模型和图像数据源的UAP生成。

积极的数据增强是视觉变压器（VIT）的强大泛化能力的关键组成部分。一种这样的数据增强技术是对抗性培训;然而，许多先前的作品表明，这通常会导致清洁的准确性差。在这项工作中，我们展示了金字塔对抗训练，这是一种简单有效的技术来提高韦维尔的整体性能。我们将其与“匹配”辍学和随机深度正则化配对，这采用了干净和对抗样品的相同辍学和随机深度配置。类似于Advprop的CNNS的改进（不直接适用于VIT），我们的金字塔对抗性训练会破坏分销准确性和vit和相关架构的分配鲁棒性之间的权衡。当Imagenet-1K数据训练时，它导致ImageNet清洁准确性的182美元的vit-B模型的精确度，同时由7美元的稳健性指标同时提高性能，从$ 1.76 \％$至11.45 \％$。我们为Imagenet-C（41.4 MCE），Imagenet-R（$ 53.92 \％$），以及Imagenet-Sketch（41.04美元\％$）的新的最先进，只使用vit-b / 16骨干和我们的金字塔对抗训练。我们的代码将在接受时公开提供。