In this paper, we introduce a new model for leveraging unlabeled data to
improve generalization performances of image classifiers: a two-branch
encoder-decoder architecture called HybridNet. The first branch receives
supervision signal and is dedicated to the extraction of invariant
class-related representations. The second branch is fully unsupervised and
dedicated to model information discarded by the first branch to reconstruct
input data. To further support the expected behavior of our model, we propose
an original training objective. It favors stability in the discriminative
branch and complementarity between the learned representations in the two
branches. HybridNet is able to outperform state-of-the-art results on CIFAR-10,
SVHN and STL-10 in various semi-supervised settings. In addition,
visualizations and ablation studies validate our contributions and the behavior
of the model on both CIFAR-10 and STL-10 datasets.

本文介绍了一种新模型：HybridNet，它是一种双分支编码器 - 解码器架构，利用未标记的图像数据改善图像分类器的泛化性能。它在各种半监督设置中优于现有技术，并提出一种新的训练目标。

HybridNet：半监督学习中的分类和重建协同

HybridNet: Classification and Reconstruction Cooperation for  Semi-Supervised Learning

One property that remains lacking in image captions generated by contemporary
methods is discriminability: being able to tell two images apart given the
caption for one of them. We propose a way to improve this aspect of caption
generation. By incorporating into the captioning training objective a loss
component directly related to ability (by a machine) to disambiguate
image/caption matches, we obtain systems that produce much more discriminative
caption, according to human evaluation. Remarkably, our approach leads to
improvement in other aspects of generated captions, reflected by a battery of
standard scores such as BLEU, SPICE etc. Our approach is modular and can be
applied to a variety of model/loss combinations commonly proposed for image
captioning.

本文提出了一种改进图像标题生成方法的方法，通过在训练过程中加入与机器学习相关的损失函数来加强图像标题的区分度，从而提高生成标题的质量。

用于训练描述性字幕的区分性目标

Discriminability objective for training descriptive captions

Few-shot learning refers to understanding new concepts from only a few
examples. We propose an information retrieval-inspired approach for this
problem that is motivated by the increased importance of maximally leveraging
all the available information in this low-data regime. We define a training
objective that aims to extract as much information as possible from each
training batch by effectively optimizing over all relative orderings of the
batch points simultaneously. In particular, we view each batch point as a
`query' that ranks the remaining ones based on its predicted relevance to them
and we define a model within the framework of structured prediction to optimize
mean Average Precision over these rankings. Our method achieves impressive
results on the standard few-shot classification benchmarks while is also
capable of few-shot retrieval.

提出一种基于信息检索的方法来解决 few-shot 学习问题，通过同时优化每个训练批次中所有相对排序以提取尽可能多的信息， 在结构化预测的框架内定义模型来优化平均精度，从而实现在标准 few-shot 分类基准测试上取得令人印象深刻的结果，并具有 few-shot 检索的能力。