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.

Representation learningLow-shot learning Feature extractor Base classes (many training examples)Classifier (base and novel categories) Novel classes (few training examples)Figure 1: Our low-shot learning benchmark in two phases: representation learning and low-shot learning. Modern recognition models use large labeled datasets like ImageNet to build good visual representations and train strong classifiers (representation learning).However, these datasets only contain a fixed set of classes. In many realistic scenarios, once deployed, the model might encounter novel classes that it also needs to recognize, but with very few training examples available (low-shot learning). We present two ways of significantly improving performance in this scenario: (1) a novel loss function for representation learning that leads to better visual representations that generalize well, and (2) a method for hallucinating additional examples for the data-starved novel classes.

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 .

The human visual system has the remarkably ability to be able to effortlessly learn novel concepts from only a few examples. Mimicking the same behavior on machine learning vision systems is an interesting and very challenging research problem with many practical advantages on real world vision applications. In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories). To achieve that goal we propose (a) to extend an object recognition system with an attention based few-shot classification weight generator, and (b) to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors. The latter, apart from unifying the recognition of both novel and base categories, it also leads to feature representations that generalize better on "unseen" categories. We extensively evaluate our approach on Mini-ImageNet where we manage to improve the prior state-of-the-art on few-shot recognition (i.e., we achieve 56.20% and 73.00% on the 1-shot and 5-shot settings respectively) while at the same time we do not sacrifice any accuracy on the base categories, which is a characteristic that most prior approaches lack. Finally, we apply our approach on the recently introduced few-shot benchmark of Bharath and Girshick [4] where we also achieve stateof-the-art results. The code and models of our paper will be published on: https://github.com/gidariss/ FewShotWithoutForgetting.

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.

在过去的几年里，几年枪支学习（FSL）引起了极大的关注，以最大限度地减少标有标记的训练示例的依赖。FSL中固有的困难是处理每个课程的培训样本太少的含糊不清的歧义。为了在FSL中解决这一基本挑战，我们的目标是培训可以利用关于新颖类别的先前语义知识来引导分类器合成过程的元学习模型。特别是，我们提出了语义调节的特征注意力和样本注意机制，估计表示尺寸和培训实例的重要性。我们还研究了FSL的样本噪声问题，以便在更现实和不完美的环境中利用Meta-Meverys。我们的实验结果展示了所提出的语义FSL模型的有效性，而没有样品噪声。

The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost. Few-shot learning is widely used as one of the standard benchmarks in meta-learning. In this work, we show that a simple baseline: learning a supervised or selfsupervised representation on the meta-training set, followed by training a linear classifier on top of this representation, outperforms state-of-the-art few-shot learning methods. An additional boost can be achieved through the use of selfdistillation. This demonstrates that using a good learned embedding model can be more effective than sophisticated meta-learning algorithms. We believe that our findings motivate a rethinking of few-shot image classification benchmarks and the associated role of meta-learning algorithms.

很少有射击学习（FSL）旨在使用有限标记的示例生成分类器。许多现有的作品采用了元学习方法，构建了一些可以从几个示例中学习以生成分类器的学习者。通常，几次学习者是通过依次对多个几次射击任务进行采样并优化几杆学习者在为这些任务生成分类器时的性能来构建或进行元训练的。性能是通过结果分类器对这些任务的测试（即查询）示例进行分类的程度来衡量的。在本文中，我们指出了这种方法的两个潜在弱点。首先，采样的查询示例可能无法提供足够的监督来进行元训练少数学习者。其次，元学习的有效性随着射击数量的增加而急剧下降。为了解决这些问题，我们为少数学习者提出了一个新颖的元训练目标，这是为了鼓励少数学习者生成像强大分类器一样执行的分类器。具体而言，我们将每个采样的几个弹药任务与强大的分类器相关联，该分类器接受了充分的标记示例。强大的分类器可以看作是目标分类器，我们希望在几乎没有示例的情况下生成的几个学习者，我们使用强大的分类器来监督少数射击学习者。我们提出了一种构建强分类器的有效方法，使我们提出的目标成为现有基于元学习的FSL方法的易于插入的术语。我们与许多代表性的元学习方法相结合验证了我们的方法，Lastshot。在几个基准数据集中，我们的方法可导致各种任务的显着改进。更重要的是，通过我们的方法，基于元学习的FSL方法可以在不同数量的镜头上胜过基于非Meta学习的方法。

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。

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。

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。

我们介绍了在Neurips'22接受的Chalearn Meta学习系列中的新挑战的设计和基线结果，重点是“跨域”元学习。元学习旨在利用从以前的任务中获得的经验，以有效地解决新任务（即具有更好的性能，较少的培训数据和/或适度的计算资源）。尽管该系列中的先前挑战集中在域内几乎没有学习问题，但目的是有效地学习n-way K-shot任务（即N级培训示例的N班级分类问题），这项竞赛挑战了参与者的解决方案。从各种领域（医疗保健，生态学，生物学，制造业等）提出的“任何通道”和“任何镜头”问题，他们是为了人道主义和社会影响而被选为。为此，我们创建了Meta-Album，这是来自10个域的40个图像分类数据集的元数据，从中，我们从中以任何数量的“方式”（在2-20范围内）和任何数量的“镜头”来解释任务”（在1-20范围内）。竞争是由代码提交的，在Codalab挑战平台上进行了完全盲目测试。获奖者的代码将是开源的，从而使自动化机器学习解决方案的部署可以在几个域中进行几次图像分类。

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.

在元学习框架下设计了许多射门学习方法，这些方法从各种学习任务中学习并推广到新任务。这些元学习方法在从同一分布（I.I.D.观察）中绘制的所有样本中的情况下实现了预期的性能。然而，在现实世界应用中，很少拍摄的学习范式往往遭受数据转移，即，即使在相同的任务中，也可以从各种数据分布中汲取不同任务中的示例。大多数现有的几次拍摄方法不考虑数据班次，因此在数据分布换档时显示降级性能。然而，由于每个任务中的标记样本数量有限的标记样本，因此在几次拍摄学习中解决数据转换问题是不普遍的。针对解决此问题，我们提出了一种新的基于度量的元学习框架，以便在知识图表的帮助下提取任务特定的表示和任务共享表示。因此，任务内的数据偏移可以通过任务共享和特定于任务的表示的组合来组合。拟议的模型是对流行的基准测试和两个构造的新具有挑战性的数据集。评估结果表明了其显着性能。

基于班级成员之间不需要普遍或恒定的特征的共享特征模式，在自然世界中很常见，并且在一系列特征上都超过了一多裂的分类。我们表明，阈值元学习者（例如原型网络）需要一个嵌入维度，该维度在与任务相关的功能数量中指数呈指数级，以模拟这些功能。相比之下，默认情况下，注意分类器（例如匹配网络）是多真的，并且能够通过线性嵌入维度解决这些问题。但是，我们发现，在存在任务核定特征的情况下，元学习问题固有的特征，注意模型容易受到错误分类的影响。为了应对这一挑战，我们提出了一种自我注意的特征选择机制，该机制可适应非歧视性特征。我们证明了我们的方法在元学习布尔功能以及合成和现实世界中的几个学习任务中的有效性。

本文解决了几秒钟学习问题，旨在从几个例子中学习新的视觉概念。在几次拍摄分类中的常见问题设置假设在获取数据标签中的随机采样策略，其在实际应用中效率低下。在这项工作中，我们介绍了一个新的预算感知几秒钟学习问题，不仅旨在学习新的对象类别，还需要选择信息实例来注释以实现数据效率。我们为我们的预算感知几秒钟学习任务开发了一个元学习策略，该任务共同了解基于图形卷积网络（GCN）和基于示例的少量拍摄分类器的新型数据选择策略。我们的选择策略通过图形消息传递计算每个未标记数据的上下文敏感表示，然后用于预测顺序选择的信息性分数。我们在迷你想象网，分层 - 想象项目和omniglot数据集上进行广泛的实验验证我们的方法。结果表明，我们的几次学习策略优于一个相当大的边缘，这表明了我们的方法的功效。

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。

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning. The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In our approach, the parameters of the model are explicitly trained such that a small number of gradient steps with a small amount of training data from a new task will produce good generalization performance on that task. In effect, our method trains the model to be easy to fine-tune. We demonstrate that this approach leads to state-of-the-art performance on two fewshot image classification benchmarks, produces good results on few-shot regression, and accelerates fine-tuning for policy gradient reinforcement learning with neural network policies.

从一个非常少数标记的样品中学习新颖的课程引起了机器学习区域的越来越高。最近关于基于元学习或转移学习的基于范例的研究表明，良好特征空间的获取信息可以是在几次拍摄任务上实现有利性能的有效解决方案。在本文中，我们提出了一种简单但有效的范式，该范式解耦了学习特征表示和分类器的任务，并且只能通过典型的传送学习培训策略从基类嵌入体系结构的特征。为了在每个类别内保持跨基地和新类别和辨别能力的泛化能力，我们提出了一种双路径特征学习方案，其有效地结合了与对比特征结构的结构相似性。以这种方式，内部级别对齐和级别的均匀性可以很好地平衡，并且导致性能提高。三个流行基准测试的实验表明，当与简单的基于原型的分类器结合起来时，我们的方法仍然可以在电感或转换推理设置中的标准和广义的几次射击问题达到有希望的结果。

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.