现代深度学习需要大规模广泛标记的数据集进行培训。少量学习旨在通过有效地从少数标记的例子中学习来缓解这个问题。在先前提出的少量视觉分类器中,假设对分类器决定的特征歧管具有不相关的特征尺寸和均匀特征方差。在这项工作中,我们专注于通过提出以低标签制度运行的差异敏感的模型来解决这一假设引起的限制。第一种方法简单的CNAP,采用基于分层正规的Mahalanobis距离基于距离的分类器,与现有神经自适应特征提取器的状态相结合,以在元数据集,迷你成像和分层图像基准基准上实现强大性能。我们进一步将这种方法扩展到转换学习设置,提出转导压盖。这种转换方法将软k-means参数细化过程与两步任务编码器相结合,以实现使用未标记数据的改进的测试时间分类精度。转导CNAP在元数据集上实现了最先进的性能。最后,我们探讨了我们的方法(简单和转换)的使用“开箱即用”持续和积极的学习。大规模基准的广泛实验表明了这一点的鲁棒性和多功能性,相对说话,简单的模型。所有培训的模型检查点和相应的源代码都已公开可用。
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很少有图像分类是一个具有挑战性的问题,旨在仅基于少量培训图像来达到人类的识别水平。少数图像分类的一种主要解决方案是深度度量学习。这些方法是,通过将看不见的样本根据距离的距离进行分类,可在强大的深神经网络中学到的嵌入空间中看到的样品,可以避免以少数图像分类的少数训练图像过度拟合,并实现了最新的图像表现。在本文中,我们提供了对深度度量学习方法的最新审查,以进行2018年至2022年的少量图像分类,并根据度量学习的三个阶段将它们分为三组,即学习功能嵌入,学习课堂表示和学习距离措施。通过这种分类法,我们确定了他们面临的不同方法和问题的新颖性。我们通过讨论当前的挑战和未来趋势进行了少量图像分类的讨论。
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We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances to prototype representations of each class. Compared to recent approaches for few-shot learning, they reflect a simpler inductive bias that is beneficial in this limited-data regime, and achieve excellent results. We provide an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning. We further extend prototypical networks to zero-shot learning and achieve state-of-theart results on the CU-Birds dataset.
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Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of catastrophic forgetting, is a hard challenge. Currently, deep learning methods can attain impressive results when the data modeled does not undergo a considerable distributional shift in subsequent learning sessions, but whenever we expose such systems to this incremental setting, performance drop very quickly. Overcoming this limitation is fundamental as it would allow us to build truly intelligent systems showing stability and plasticity. Secondly, it would allow us to overcome the onerous limitation of retraining these architectures from scratch with the new updated data. In this thesis, we tackle the problem from multiple directions. In a first study, we show that in rehearsal-based techniques (systems that use memory buffer), the quantity of data stored in the rehearsal buffer is a more important factor over the quality of the data. Secondly, we propose one of the early works of incremental learning on ViTs architectures, comparing functional, weight and attention regularization approaches and propose effective novel a novel asymmetric loss. At the end we conclude with a study on pretraining and how it affects the performance in Continual Learning, raising some questions about the effective progression of the field. We then conclude with some future directions and closing remarks.
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Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100. Our code is publicly available at https://github.com/ElementAI/TADAM.
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epiSodic学习是对几枪学习感兴趣的研究人员和从业者的流行练习。它包括在一系列学习问题(或剧集)中组织培训,每个人分为小型训练和验证子集,以模仿评估期间遇到的情况。但这总是必要吗?在本文中,我们调查了在集发作的级别使用非参数方法,例如最近邻居等方法的焦点学习的有用性。对于这些方法,我们不仅展示了广州学习的限制是如何不必要的,而是他们实际上导致利用培训批次的数据低效方式。我们通过匹配和原型网络进行广泛的消融实验,其中两个最流行的方法在集中的级别使用非参数方法。他们的“非焦化”对应物具有很大的更简单,具有较少的近似参数,并在多个镜头分类数据集中提高它们的性能。
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最近,已经观察到,转移学习解决方案可能是我们解决许多少量学习基准的全部 - 因此提出了有关何时以及如何部署元学习算法的重要问题。在本文中,我们试图通过1.提出一个新颖的指标(多样性系数)来阐明这些问题,以测量几次学习基准和2.的任务多样性。 )并在公平条件下进行学习(相同的体系结构,相同的优化器和所有经过培训的模型)。使用多样性系数,我们表明流行的迷你胶原和Cifar-fs几乎没有学习基准的多样性低。这种新颖的洞察力将转移学习解决方案比在公平比较的低多样性方面的元学习解决方案更好。具体而言,我们从经验上发现,低多样性系数与转移学习和MAML学习解决方案之间的高相似性在元测试时间和分类层相似性方面(使用基于特征的距离指标,例如SVCCA,PWCCA,CKA和OPD) )。为了进一步支持我们的主张,我们发现这种元测试的准确性仍然存在,即使模型大小变化也是如此。因此,我们得出的结论是,在低多样性制度中,MAML和转移学习在公平比较时具有等效的元检验性能。我们也希望我们的工作激发了对元学习基准测试基准的更周到的结构和定量评估。
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现代ML方法在培训数据是IID,大规模和良好标记的时候Excel。在不太理想的条件下学习仍然是一个开放的挑战。在不利条件下,几次射击,持续的,转移和代表学习的子场在学习中取得了很大的进步;通过方法和见解,每个都提供了独特的优势。这些方法解决了不同的挑战,例如依次到达的数据或稀缺的训练示例,然而,在部署之前,ML系统将面临困难的条件。因此,需要可以处理实际设置中许多学习挑战的一般ML系统。为了促进一般ML方法目标的研究,我们介绍了一个新的统一评估框架 - 流体(灵活的顺序数据)。流体集成了几次拍摄,持续的,转移和表示学习的目标,同时能够比较和整合这些子场的技术。在流体中,学习者面临数据流,并且必须在选择如何更新自身时进行顺序预测,快速调整到新颖的类别,并处理更改的数据分布;虽然会计计算总额。我们对广泛的方法进行实验,这些方法阐述了新的洞察当前解决方案的优缺点并表明解决了新的研究问题。作为更一般方法的起点,我们展示了两种新的基线,其在流体上优于其他评估的方法。项目页面:https://raivn.cs.washington.edu/projects/fluid/。
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很少的识别涉及训练图像分类器,以使用几个示例(Shot)在测试时间区分新颖概念。现有方法通常假定测试时间的射击号是事先知道的。这是不现实的,当火车和测试射击不匹配时,流行和基础方法的性能已被证明会受到影响。我们对该现象进行了系统的经验研究。与先前的工作一致,我们发现射击灵敏度在基于度量的几个学习者中广泛存在,但是与先前的工作相反,较大的神经体系结构为变化的测试拍摄提供了一定程度的内置鲁棒性。更重要的是,通过消除对样品噪声的敏感性,一种基于余弦距离的简单,以前已知但非常忽略了一类方法,可以极大地改善对射击变化的鲁​​棒性。我们为流行和最近的几个弹药分类器提供了余弦替代品,从而扩大了它们对现实环境的适用性。这些余弦模型一致地提高了射击力,超越先前的射击状态,并在一系列基准和架构上提供竞争精度,包括在非常低的射击方案中取得的显着增长。
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大多数元学习方法都假设存在于可用于基本知识的情节元学习的一组非常大的标记数据。这与更现实的持续学习范例形成对比,其中数据以包含不相交类的任务的形式逐步到达。在本文中,我们考虑了这个增量元学习(IML)的这个问题,其中类在离散任务中逐步呈现。我们提出了一种方法,我们调用了IML,我们称之为eCISODIC重播蒸馏(ERD),该方法将来自当前任务的类混合到当前任务中,当研究剧集时,来自先前任务的类别示例。然后将这些剧集用于知识蒸馏以最大限度地减少灾难性的遗忘。四个数据集的实验表明ERD超越了最先进的。特别是,在一次挑战的单次次数较挑战,长任务序列增量元学习场景中,我们将IML和联合训练与当前状态的3.5%/ 10.1%/ 13.4%之间的差距降低我们在Diered-ImageNet / Mini-ImageNet / CIFAR100上分别为2.6%/ 2.9%/ 5.0%。
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很少有视觉识别是指从一些标记实例中识别新颖的视觉概念。通过将查询表示形式与类表征进行比较以预测查询实例的类别,许多少数射击的视觉识别方法采用了基于公制的元学习范式。但是,当前基于度量的方法通常平等地对待所有实例,因此通常会获得有偏见的类表示,考虑到并非所有实例在总结了类级表示的实例级表示时都同样重要。例如,某些实例可能包含无代表性的信息,例如过多的背景和无关概念的信息,这使结果偏差。为了解决上述问题,我们提出了一个新型的基于公制的元学习框架,称为实例自适应类别表示网络(ICRL-net),以进行几次视觉识别。具体而言,我们开发了一个自适应实例重新平衡网络,具有在生成班级表示,通过学习和分配自适应权重的不同实例中的自适应权重时,根据其在相应类的支持集中的相对意义来解决偏见的表示问题。此外,我们设计了改进的双线性实例表示,并结合了两个新型的结构损失,即,阶层内实例聚类损失和阶层间表示区分损失,以进一步调节实例重估过程并完善类表示。我们对四个通常采用的几个基准测试:Miniimagenet,Tieredimagenet,Cifar-FS和FC100数据集进行了广泛的实验。与最先进的方法相比,实验结果证明了我们的ICRL-NET的优势。
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Learning with limited data is a key challenge for visual recognition. Many few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them leads to the target task. In this paper, we propose a novel approach to adapt the instance embeddings to the target classification task with a set-to-set function, yielding embeddings that are task-specific and are discriminative. We empirically investigated various instantiations of such set-to-set functions and observed the Transformer is most effective -as it naturally satisfies key properties of our desired model. We denote this model as FEAT (few-shot embedding adaptation w/ Transformer) and validate it on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases, i.e., cross-domain, transductive, generalized few-shot learning, and low-shot learning. It archived consistent improvements over baseline models as well as previous methods, and established the new stateof-the-art results on two benchmarks.
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Metric-based meta-learning is one of the de facto standards in few-shot learning. It composes of representation learning and metrics calculation designs. Previous works construct class representations in different ways, varying from mean output embedding to covariance and distributions. However, using embeddings in space lacks expressivity and cannot capture class information robustly, while statistical complex modeling poses difficulty to metric designs. In this work, we use tensor fields (``areas'') to model classes from the geometrical perspective for few-shot learning. We present a simple and effective method, dubbed hypersphere prototypes (HyperProto), where class information is represented by hyperspheres with dynamic sizes with two sets of learnable parameters: the hypersphere's center and the radius. Extending from points to areas, hyperspheres are much more expressive than embeddings. Moreover, it is more convenient to perform metric-based classification with hypersphere prototypes than statistical modeling, as we only need to calculate the distance from a data point to the surface of the hypersphere. Following this idea, we also develop two variants of prototypes under other measurements. Extensive experiments and analysis on few-shot learning tasks across NLP and CV and comparison with 20+ competitive baselines demonstrate the effectiveness of our approach.
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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.
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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。
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从少数样本中学习的多功能性是人类智能的标志。很少有学习能力超越机器的努力。受概率深度学习的承诺和力量的启发,我们提出了一个新颖的变异推理网络,用于几个射击分类(被构成三叉戟),将图像的表示形式分离为语义和标记潜在变量,并以相互交织的方式推断它们。为了诱导任务意识,作为三叉戟推理机制的一部分,我们使用一种新型的基于内置的基于注意的转导功能提取模块(我们致电ATTFEX)在查询和支持图像上借鉴了几次任务的图像。我们广泛的实验结果证实了三叉戟的功效,并证明,使用最简单的骨架,它在最常见的数据集Miniimagenet和Tieredimagenet中设置了新的最新时间(最多可提高4%和5%,,高达4%和5%分别是)以及最近具有挑战性的跨域迷你膜 - > CUB场景,其范围超出了最佳现有跨域基线的显着利润率(最高20%)。可以在我们的GitHub存储库中找到代码和实验:https://github.com/anujinho/trident
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我们考虑了一个新颖的表述,即主动射击分类(AFSC)的问题,其目的是对标签预算非常限制的小规定,最初未标记的数据集进行分类。这个问题可以看作是与经典的跨托管少数射击分类(TFSC)的竞争对手范式,因为这两种方法都适用于相似的条件。我们首先提出了一种结合统计推断的方法,以及一种非常适合该框架的原始两级积极学习策略。然后,我们从TFSC领域调整了几个标准视觉基准。我们的实验表明,AFSC的潜在优势可能是很大的,与最先进的TFSC方法相比,对于同一标签预算,平均加权准确性高达10%。我们认为,这种新的范式可能会导致数据筛选学习设置的新发展和标准。
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Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts might help machine vision systems perform better low-shot learning, i.e., learning concepts from few examples. We present a novel approach to low-shot learning that uses this idea. Our approach builds on recent progress in meta-learning ("learning to learn") by combining a meta-learner with a "hallucinator" that produces additional training examples, and optimizing both models jointly. Our hallucinator can be incorporated into a variety of meta-learners and provides significant gains: up to a 6 point boost in classification accuracy when only a single training example is available, yielding state-of-the-art performance on the challenging ImageNet low-shot classification benchmark.
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我们研究了用于半监控学习(SSL)的无监督数据选择,其中可以提供大规模的未标记数据集,并且为标签采集预算小额数据子集。现有的SSL方法专注于学习一个有效地集成了来自给定小标记数据和大型未标记数据的信息的模型,而我们专注于选择正确的数据以用于SSL的注释,而无需任何标签或任务信息。直观地,要标记的实例应统称为下游任务的最大多样性和覆盖范围,并且单独具有用于SSL的最大信息传播实用程序。我们以三步数据为中心的SSL方法形式化这些概念,使稳定性和精度的纤维液改善8%的CiFar-10(标记为0.08%)和14%的Imagenet -1k(标记为0.2%)。它也是一种具有各种SSL方法的通用框架,提供一致的性能增益。我们的工作表明,在仔细选择注释数据上花费的小计算带来了大注释效率和模型性能增益,而无需改变学习管道。我们完全无监督的数据选择可以轻松扩展到其他弱监督的学习设置。
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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。
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