我们介绍了一种计算关于数据集的学习任务的导数的方法。学习任务是从训练设置到验证错误的函数，可以由培训的深神经网络（DNN）表示。 “数据集导数”是一个线性运算符，围绕培训的模型计算，它通知每个训练样本的权重的扰动如何影响验证误差，通常在单独的验证数据集上计算。我们的方法，DIVA（可微分验证）铰接在预先训练的DNN周围的休假交叉验证误差的闭合形式微分表达上。这种表达构成数据集衍生物。 Diva可用于数据集自动策策，例如用错误的注释删除样本，使用其他相关样本增强数据集或重新平衡。更一般地，DIVA可用于优化数据集，以及模型的参数，作为培训过程的一部分，而无需单独的验证数据集，与AutomL的双层优化方法不同。为了说明DIVA的灵活性，我们向样本自动策展任务报告实验，如异常值拒绝，数据集扩展和多模态数据的自动聚合。

Few-shot learning (FSL) is a central problem in meta-learning, where learners must efficiently learn from few labeled examples. Within FSL, feature pre-training has recently become an increasingly popular strategy to significantly improve generalization performance. However, the contribution of pre-training is often overlooked and understudied, with limited theoretical understanding of its impact on meta-learning performance. Further, pre-training requires a consistent set of global labels shared across training tasks, which may be unavailable in practice. In this work, we address the above issues by first showing the connection between pre-training and meta-learning. We discuss why pre-training yields more robust meta-representation and connect the theoretical analysis to existing works and empirical results. Secondly, we introduce Meta Label Learning (MeLa), a novel meta-learning algorithm that learns task relations by inferring global labels across tasks. This allows us to exploit pre-training for FSL even when global labels are unavailable or ill-defined. Lastly, we introduce an augmented pre-training procedure that further improves the learned meta-representation. Empirically, MeLa outperforms existing methods across a diverse range of benchmarks, in particular under a more challenging setting where the number of training tasks is limited and labels are task-specific. We also provide extensive ablation study to highlight its key properties.

We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective. Depending on the specific setting, the outer variables take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We provide sufficient conditions under which solutions of the approximate problem converge to those of the exact problem. We instantiate our approach for meta-learning in the case of deep learning where representation layers are treated as hyperparameters shared across a set of training episodes. In experiments, we confirm our theoretical findings, present encouraging results for few-shot learning and contrast the bilevel approach against classical approaches for learning-to-learn.

Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively trained linear predictors can offer better generalization. We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks. Our objective is to learn feature embeddings that generalize well under a linear classification rule for novel categories. To efficiently solve the objective, we exploit two properties of linear classifiers: implicit differentiation of the optimality conditions of the convex problem and the dual formulation of the optimization problem. This allows us to use highdimensional embeddings with improved generalization at a modest increase in computational overhead. Our approach, named MetaOptNet, achieves state-of-the-art performance on miniImageNet, tieredImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks. Our code is available online 1 .

近年来，计算机视觉社区中最受欢迎的技术之一就是深度学习技术。作为一种数据驱动的技术，深层模型需要大量准确标记的培训数据，这在许多现实世界中通常是无法访问的。数据空间解决方案是数据增强（DA），可以人为地从原始样本中生成新图像。图像增强策略可能因数据集而有所不同，因为不同的数据类型可能需要不同的增强以促进模型培训。但是，DA策略的设计主要由具有领域知识的人类专家决定，这被认为是高度主观和错误的。为了减轻此类问题，一个新颖的方向是使用自动数据增强（AUTODA）技术自动从给定数据集中学习图像增强策略。 Autoda模型的目的是找到可以最大化模型性能提高的最佳DA策略。这项调查从图像分类的角度讨论了Autoda技术出现的根本原因。我们确定标准自动赛车模型的三个关键组件：搜索空间，搜索算法和评估功能。根据他们的架构，我们提供了现有图像AUTODA方法的系统分类法。本文介绍了Autoda领域的主要作品，讨论了他们的利弊，并提出了一些潜在的方向以进行未来的改进。

Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising direction for reducing training cost is dataset condensation that aims to replace the original large training set with a significantly smaller learned synthetic set while preserving the original information. While training deep models on the small set of condensed images can be extremely fast, their synthesis remains computationally expensive due to the complex bi-level optimization and second-order derivative computation. In this work, we propose a simple yet effective method that synthesizes condensed images by matching feature distributions of the synthetic and original training images in many sampled embedding spaces. Our method significantly reduces the synthesis cost while achieving comparable or better performance. Thanks to its efficiency, we apply our method to more realistic and larger datasets with sophisticated neural architectures and obtain a significant performance boost. We also show promising practical benefits of our method in continual learning and neural architecture search.

Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.

适应数据分布的结构（例如对称性和转型Imarerces）是机器学习中的重要挑战。通过架构设计或通过增强数据集，可以内在学习过程中内置Inhormces。两者都需要先验的了解对称性的确切性质。缺乏这种知识，从业者求助于昂贵且耗时的调整。为了解决这个问题，我们提出了一种新的方法来学习增强变换的分布，以新的\ emph {转换风险最小化}（trm）框架。除了预测模型之外，我们还优化了从假说空间中选择的转换。作为算法框架，我们的TRM方法是（1）有效（共同学习增强和模型，以\ emph {单训练环}），（2）模块化（使用\ emph {任何训练算法），以及（3）一般（处理\ \ ich {离散和连续}增强）。理论上与标准风险最小化的TRM比较，并在其泛化误差上给出PAC-Bayes上限。我们建议通过块组成的新参数化优化富裕的增强空间，导致新的\ EMPH {随机成分增强学习}（SCALE）算法。我们在CIFAR10 / 100，SVHN上使用先前的方法（快速自身自动化和武术器）进行实际比较规模。此外，我们表明规模可以在数据分布中正确地学习某些对称性（恢复旋转Mnist上的旋转），并且还可以改善学习模型的校准。

监督学习的关键假设是培训和测试数据遵循相同的概率分布。然而，这种基本假设在实践中并不总是满足，例如，由于不断变化的环境，样本选择偏差，隐私问题或高标签成本。转移学习（TL）放松这种假设，并允许我们在分销班次下学习。通常依赖于重要性加权的经典TL方法 - 基于根据重要性（即测试过度训练密度比率）的训练损失培训预测器。然而，由于现实世界机器学习任务变得越来越复杂，高维和动态，探讨了新的新方法，以应对这些挑战最近。在本文中，在介绍基于重要性加权的TL基础之后，我们根据关节和动态重要预测估计审查最近的进步。此外，我们介绍一种因果机制转移方法，该方法包含T1中的因果结构。最后，我们讨论了TL研究的未来观点。

In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy labels during the training, completely removing their contribution. This discarding process prevents DNNs from learning wrong associations between images and their correct labels but reduces the amount of data used, especially when most of the samples have noisy labels. Differently, our method weighs the feature extracted directly from the classifier without altering the loss value of each data. The advisor helps to focus only on some part of the information present in mislabeled examples, allowing the classifier to leverage that data as well. We trained it with a meta-learning strategy so that it can adapt throughout the training of the main model. We tested our method on CIFAR10 and CIFAR100 with synthetic noise, and on Clothing1M which contains real-world noise, reporting state-of-the-art results.

一种广泛使用的传输学习算法是微调的，其中预先接受的模型在具有少量标记数据的目标任务上进行微调。当预训练模型的容量大于目标数据集的大小时，微调容易过度，并“记忆”训练标签。因此，一个重要的问题是规范微调，并确保其对噪声的鲁棒性。为了解决这个问题，我们首先分析微调的泛化属性。我们介绍了PAC-Bayes泛化界定，这取决于在微调和微调模型的噪声稳定期间在每层中行进的距离。我们经验衡量这些数量。根据分析，我们建议正规化的自我标签 - 正规化和自我标记方法之间的插值，包括（i）层明智的正则化，以限制在每层中行进的距离; （ii）自我标记 - 纠正和标签重新重复纠正错误标记的数据点（模型是自信的）和重新重复的自信数据点。我们在使用多个预先训练的模型体系结构上验证我们的方法和文本数据集的广泛集合和文本数据集。我们的方法将基线方法提高了1.76％（平均），可实现七种图像分类任务和0.75％，为几次拍摄的分类任务。当目标数据集包括嘈杂的标签时，我们的方法在两个嘈杂的设置中平均优于基线方法3.56％。

机器学习模型通常会遇到与训练分布不同的样本。无法识别分布（OOD）样本，因此将该样本分配给课堂标签会显着损害模​​型的可靠性。由于其对在开放世界中的安全部署模型的重要性，该问题引起了重大关注。由于对所有可能的未知分布进行建模的棘手性，检测OOD样品是具有挑战性的。迄今为止，一些研究领域解决了检测陌生样本的问题，包括异常检测，新颖性检测，一级学习，开放式识别识别和分布外检测。尽管有相似和共同的概念，但分别分布，开放式检测和异常检测已被独立研究。因此，这些研究途径尚未交叉授粉，创造了研究障碍。尽管某些调查打算概述这些方法，但它们似乎仅关注特定领域，而无需检查不同领域之间的关系。这项调查旨在在确定其共同点的同时，对各个领域的众多著名作品进行跨域和全面的审查。研究人员可以从不同领域的研究进展概述中受益，并协同发展未来的方法。此外，据我们所知，虽然进行异常检测或单级学习进行了调查，但没有关于分布外检测的全面或最新的调查，我们的调查可广泛涵盖。最后，有了统一的跨域视角，我们讨论并阐明了未来的研究线，打算将这些领域更加紧密地融为一体。

在新课程训练时，几乎没有射击学习（FSL）方法通常假设具有准确标记的样品的清洁支持集。这个假设通常可能是不现实的：支持集，无论多么小，仍然可能包括标签错误的样本。因此，对标签噪声的鲁棒性对于FSL方法是实用的，但是这个问题令人惊讶地在很大程度上没有探索。为了解决FSL设置中标签错误的样品，我们做出了一些技术贡献。 （1）我们提供了简单而有效的特征聚合方法，改善了流行的FSL技术Protonet使用的原型。 （2）我们描述了一种嘈杂的噪声学习的新型变压器模型（TRANFS）。 TRANFS利用变压器的注意机制称重标记为错误的样品。 （3）最后，我们对迷你胶原和tieredimagenet的嘈杂版本进行了广泛的测试。我们的结果表明，TRANFS与清洁支持集的领先FSL方法相对应，但到目前为止，在存在标签噪声的情况下，它们的表现优于它们。

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.

作为主导范式，微调目标数据的预先训练模型广泛用于许多深度学习应用，特别是对于小数据集。然而，最近的研究已经明确表明，一旦培训迭代的数量增加，划痕训练都没有比这一训练前策略更糟糕的最终表现。在这项工作中，我们从学习理论中流行的泛化分析的角度重新审视这种现象。我们的结果表明，最终预测精度可能具有对预训练模型的弱依赖性，特别是在大训练迭代的情况下。观察激励我们利用预训练预调整的数据，因为此数据也可用于微调。使用预训练数据的泛化结果表明，当适当的预训练数据包含在微调中时，可以提高目标任务的最终性能。随着理论发现的洞察力，我们提出了一种新颖的选择策略来选择从预训练数据中的子集，以帮助改善目标任务的概括。 8个基准数据集上的图像分类任务的广泛实验结果验证了基于数据选择的微调管道的有效性。

Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called Men-torNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on We-bVision, a large benchmark containing 2.2 million images of real-world noisy labels. The code are at https://github.com/google/mentornet.

现代机器学习问题中的不平衡数据集是司空见惯的。具有敏感属性的代表性课程或群体的存在导致关于泛化和公平性的担忧。这种担忧进一步加剧了大容量深网络可以完全适合培训数据，似乎在训练期间达到完美的准确性和公平，但在测试期间表现不佳。为了解决这些挑战，我们提出了自动化，一个自动设计培训损失功能的双层优化框架，以优化准确性和寻求公平目标的混合。具体地，较低级别的问题列举了模型权重，并且上级问题通过监视和优化通过验证数据的期望目标来调谐损耗功能。我们的损耗设计通过采用参数跨熵损失和个性化数据增强方案，可以为类/组进行个性化处理。我们评估我们对不平衡和群体敏感分类的应用方案的方法的好处和性能。广泛的经验评估表明了自动矛盾最先进的方法的益处。我们的实验结果与损耗功能设计的理论见解和培训验证分裂的好处相辅相成。所有代码都是可用的开源。

预训练（PT），然后进行微调（FT）是培训神经网络的有效方法，并导致许多域中的显着性能改进。 PT可以包含各种设计选择，如任务和数据重新免除策略，增强政策和噪声模型，所有这些都可以显着影响所学到的陈述的质量。因此，必须适当地调整这些策略引入的超级参数。但是，设置这些超参数的值是具有挑战性的。大多数现有方法都努力缩放到高维度，太慢和内存密集，或者不能直接应用于两级PT和FT学习过程。在这项工作中，我们提出了一种基于渐变的梯度的算法，以Meta-Learn PT HyperParameters。我们将PT HyperParameter优化问题正式化，并提出了一种通过展开优化结合隐式分化和反向来获得PT超级参数梯度的新方法。我们展示了我们的方法可以提高两个真实域的预测性能。首先，我们优化高维任务加权超参数，用于多任务对蛋白质 - 蛋白质相互作用图进行培训，并将Auroc提高至3.9％。其次，我们在心电图数据上优化用于SIMCLR的SIMCLR的数据增强神经网络，并将Auroc提高到1.9％。

大多数机器学习算法由一个或多个超参数配置，必须仔细选择并且通常会影响性能。为避免耗时和不可递销的手动试验和错误过程来查找性能良好的超参数配置，可以采用各种自动超参数优化（HPO）方法，例如，基于监督机器学习的重新采样误差估计。本文介绍了HPO后，本文审查了重要的HPO方法，如网格或随机搜索，进化算法，贝叶斯优化，超带和赛车。它给出了关于进行HPO的重要选择的实用建议，包括HPO算法本身，性能评估，如何将HPO与ML管道，运行时改进和并行化结合起来。这项工作伴随着附录，其中包含关于R和Python的特定软件包的信息，以及用于特定学习算法的信息和推荐的超参数搜索空间。我们还提供笔记本电脑，这些笔记本展示了这项工作的概念作为补充文件。

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.