Few-shot classification addresses the challenge of classifying examples given
only limited labeled data. A powerful approach is to go beyond data
augmentation, towards data synthesis. However, most of data
augmentation/synthesis methods for few-shot classification are overly complex
and sophisticated, e.g. training a wGAN with multiple regularizers or training
a network to transfer latent diversities from known to novel classes. We make
two contributions, namely we show that: (1) using a simple loss function is
more than enough for training a feature generator in the few-shot setting; and
(2) learning to generate tensor features instead of vector features is
superior. Extensive experiments on miniImagenet, CUB and CIFAR-FS datasets show
that our method sets a new state of the art, outperforming more sophisticated
few-shot data augmentation methods.

本文提出了一种使用简单的损失函数训练特征生成器，在数据合成过程中生成张量特征而不是向量特征的方法，实验证明该方法在少样本分类中表现优异，超过了更复杂的数据增强方法，成为新的最先进技术。

张量幻觉下的少样本学习

Few-shot learning via tensor hallucination

This paper studies the problem of generalized zero-shot learning which
requires the model to train on image-label pairs from some seen classes and
test on the task of classifying new images from both seen and unseen classes.
Most previous models try to learn a fixed one-directional mapping between
visual and semantic space, while some recently proposed generative methods try
to generate image features for unseen classes so that the zero-shot learning
problem becomes a traditional fully-supervised classification problem. In this
paper, we propose a novel model that provides a unified framework for three
different approaches: visual-> semantic mapping, semantic->visual mapping, and
metric learning. Specifically, our proposed model consists of a feature
generator that can generate various visual features given class embeddings as
input, a regressor that maps each visual feature back to its corresponding
class embedding, and a discriminator that learns to evaluate the closeness of
an image feature and a class embedding. All three components are trained under
the combination of cyclic consistency loss and dual adversarial loss.
Experimental results show that our model not only preserves higher accuracy in
classifying images from seen classes, but also performs better than existing
state-of-the-art models in in classifying images from unseen classes.

本文研究了广义零样学习问题，提出了一种新的模型联合了三种不同的方法：视觉 -> 语义映射，语义 -> 视觉映射和度量学习，并且将它们统一在一个框架内，最终的实验结果表明该模型不仅在分类已知类别的图像上具有较高的准确性，而且在分类未知类别的图像上性能也比现有的最先进模型要更好。

泛零样本学习生成对抗网络

Generative Dual Adversarial Network for Generalized Zero-shot Learning

Numerous algorithms have been proposed for transferring knowledge from a
label-rich domain (source) to a label-scarce domain (target). Almost all of
them are proposed for a closed-set scenario, where the source and the target
domain completely share the class of their samples. We call the shared class
the \doublequote{known class.} However, in practice, when samples in target
domain are not labeled, we cannot know whether the domains share the class. A
target domain can contain samples of classes that are not shared by the source
domain. We call such classes the \doublequote{unknown class} and algorithms
that work well in the open set situation are very practical. However, most
existing distribution matching methods for domain adaptation do not work well
in this setting because unknown target samples should not be aligned with the
source.
In this paper, we propose a method for an open set domain adaptation scenario
which utilizes adversarial training. A classifier is trained to make a boundary
between the source and the target samples whereas a generator is trained to
make target samples far from the boundary. Thus, we assign two options to the
feature generator: aligning them with source known samples or rejecting them as
unknown target samples. This approach allows extracting features that separate
unknown target samples from known target samples. Our method was extensively
evaluated in domain adaptation setting and outperformed other methods with a
large margin in most settings.

该研究提出了一种利用对抗训练的开放式域自适应方法，其中分类器被训练用于在源与目标样本之间建立边界，而生成器被训练用于使目标样本远离边界，从而摆脱未知的目标样本，该方法在领域适应设置中得到了广泛的评估，并在大多数设置中优于其他方法。