Discriminatively trained, deterministic neural networks are the de facto
choice for classification problems. However, even though they achieve
state-of-the-art results on in-domain test sets, they tend to be overconfident
on out-of-distribution (OOD) data. For instance, ReLU networks -- a popular
class of neural network architectures -- have been shown to almost always yield
high confidence predictions when the test data are far away from the training
set, even when they are trained with OOD data. We overcome this problem by
adding a term to the output of the neural network that corresponds to the logit
of an extra class, that we design to dominate the logits of the original
classes as we move away from the training data.This technique provably prevents
arbitrarily high confidence on far-away test data while maintaining a simple
discriminative point-estimate training. Evaluation on various benchmarks
demonstrates strong performance against competitive baselines on both far-away
and realistic OOD data.

通过在神经网络输出中添加额外类别的逻辑回归值，我们解决了过度自信并提高对边界测试数据的性能表现。

预防在点估计判别神经网络中对远离数据的任意高自信度

Preventing Arbitrarily High Confidence on Far-Away Data in  Point-Estimated Discriminative Neural Networks

Datasets for training object recognition systems are steadily increasing in
size. This paper investigates the question of whether existing detectors will
continue to improve as data grows, or saturate in performance due to limited
model complexity and the Bayes risk associated with the feature spaces in which
they operate. We focus on the popular paradigm of discriminatively trained
templates defined on oriented gradient features. We investigate the performance
of mixtures of templates as the number of mixture components and the amount of
training data grows. Surprisingly, even with proper treatment of regularization
and "outliers", the performance of classic mixture models appears to saturate
quickly ($\sim$10 templates and $\sim$100 positive training examples per
template). This is not a limitation of the feature space as compositional
mixtures that share template parameters via parts and that can synthesize new
templates not encountered during training yield significantly better
performance. Based on our analysis, we conjecture that the greatest gains in
detection performance will continue to derive from improved representations and
learning algorithms that can make efficient use of large datasets.

本文研究了对象识别系统的模型对各种类型数据大小的适应能力，主要使用了有向梯度特征的磨损模板以及合成的模板混合模型作为研究对象，并发现了混合模型的数量和正样本数量很容易饱和，提出了利用组合混合模型的方法进行训练和预测以及在大数据集上学习效率的问题。

我们需要更多训练数据吗？

Do We Need More Training Data?

This paper addresses the challenge of establishing a bridge between deep
convolutional neural networks and conventional object detection frameworks for
accurate and efficient generic object detection. We introduce Dense Neural
Patterns, short for DNPs, which are dense local features derived from
discriminatively trained deep convolutional neural networks. DNPs can be easily
plugged into conventional detection frameworks in the same way as other dense
local features(like HOG or LBP). The effectiveness of the proposed approach is
demonstrated with the Regionlets object detection framework. It achieved 46.1%
mean average precision on the PASCAL VOC 2007 dataset, and 44.1% on the PASCAL
VOC 2010 dataset, which dramatically improves the original Regionlets approach
without DNPs.

通过引入从深度卷积神经网络中获取的密集局部特征 —— 密集神经模式 (DNPs)，这篇文章提出了一种准确高效的一般物体检测方法。该方法在现有的检测框架中使用 DNPs，取得了显著的进展。