In this paper, we introduce a novel discriminative loss function with large
margin in the context of Deep Learning. This loss boosts the discriminative
power of neural nets, represented by intra-class compactness and inter-class
separability. On the one hand, the class compactness is ensured by close
distance of samples of the same class to each other. On the other hand, the
inter-class separability is boosted by a margin loss that ensures the minimum
distance of each class to its closest boundary. All the terms in our loss have
an explicit meaning, giving a direct view of the feature space obtained. We
analyze mathematically the relation between compactness and margin term, giving
a guideline about the impact of the hyper-parameters on the learned features.
Moreover, we also analyze properties of the gradient of the loss with respect
to the parameters of the neural net. Based on this, we design a strategy called
partial momentum updating that enjoys simultaneously stability and consistency
in training. Furthermore, we also investigate generalization errors to have
better theoretical insights. Our loss function systematically boosts the test
accuracy of models compared to the standard softmax loss in our experiments.

这篇论文介绍了一种在深度学习背景下具有较大边界的新型判别性损失函数，通过提高神经网络的判别能力，即类内紧凑性和类间可分性，来优化特征空间，同时通过数学分析关系、设计策略和研究泛化误差，提高模型的测试准确性。

分类的大边缘判别损失

Large Margin Discriminative Loss for Classification

In deep neural network, the cross-entropy loss function is commonly used for
classification. Minimizing cross-entropy is equivalent to maximizing likelihood
under assumptions of uniform feature and class distributions. It belongs to
generative training criteria which does not directly discriminate correct class
from competing classes. We propose a discriminative loss function with negative
log likelihood ratio between correct and competing classes. It significantly
outperforms the cross-entropy loss on the CIFAR-10 image classification task.

本文提出了一种判别损失函数，与生成式训练标准不同，该函数使用正确和竞争类之间的负对数似然比，极大地提高了在 CIFAR-10 图像分类任务中的性能。

深度神经网络分类的负对数似然比损失函数

Negative Log Likelihood Ratio Loss for Deep Neural Network  Classification

Semantic instance segmentation remains a challenging task. In this work we
propose to tackle the problem with a discriminative loss function, operating at
the pixel level, that encourages a convolutional network to produce a
representation of the image that can easily be clustered into instances with a
simple post-processing step. The loss function encourages the network to map
each pixel to a point in feature space so that pixels belonging to the same
instance lie close together while different instances are separated by a wide
margin. Our approach of combining an off-the-shelf network with a principled
loss function inspired by a metric learning objective is conceptually simple
and distinct from recent efforts in instance segmentation. In contrast to
previous works, our method does not rely on object proposals or recurrent
mechanisms. A key contribution of our work is to demonstrate that such a simple
setup without bells and whistles is effective and can perform on par with more
complex methods. Moreover, we show that it does not suffer from some of the
limitations of the popular detect-and-segment approaches. We achieve
competitive performance on the Cityscapes and CVPPP leaf segmentation
benchmarks.

本文提出利用判别式损失函数的像素级卷积神经网络来实现简单的图像实例分割，不需要借助物体提议或循环机制，仅仅通过一个后处理步骤即可完成聚类，简单有效且具有竞争性。