Humans are able to explain their reasoning. On the contrary, deep neural
networks are not. This paper attempts to bridge this gap by introducing a new
way to design interpretable neural networks for classification, inspired by
physiological evidence of the human visual system's inner-workings. This paper
proposes a neural network design paradigm, termed InterpNET, which can be
combined with any existing classification architecture to generate natural
language explanations of the classifications. The success of the module relies
on the assumption that the network's computation and reasoning is represented
in its internal layer activations. While in principle InterpNET could be
applied to any existing classification architecture, it is evaluated via an
image classification and explanation task. Experiments on a CUB bird
classification and explanation dataset show qualitatively and quantitatively
that the model is able to generate high-quality explanations. While the current
state-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves a
much higher METEOR score of 37.9.

本文提出了一种新的解释性神经网络框架，可以生成自然语言的分类解释，试图弥合人类推理和深度神经网络推理相差的鸿沟。该模型在 CUB 鸟类分类和解释数据集上获得了 METEOR 分数 37.9，为目前最高水平。

InterpNET: 可解释深度学习的神经内视

InterpNET: Neural Introspection for Interpretable Deep Learning

We study and compare the human visual system and state-of-the-art deep neural
networks on classification of distorted images. Different from previous works,
we limit the display time to 100ms to test only the early mechanisms of the
human visual system, without allowing time for any eye movements or other
higher level processes. Our findings show that the human visual system still
outperforms modern deep neural networks under blurry and noisy images. These
findings motivate future research into developing more robust deep networks.

本文研究并比较了人类视觉系统和最先进的深度神经网络在分类扭曲图像方面的表现，发现在模糊和嘈杂的图像条件下，仍然表现出人类视觉系统更加优越的性能，这些发现激励未来研究开发更加稳健的深度网络。

早期人类视觉系统能否与深度神经网络竞争？

Can the early human visual system compete with Deep Neural Networks?

Although the human visual system can recognize many concepts under
challenging conditions, it still has some biases. In this paper, we investigate
whether we can extract these biases and transfer them into a machine
recognition system. We introduce a novel method that, inspired by well-known
tools in human psychophysics, estimates the biases that the human visual system
might use for recognition, but in computer vision feature spaces. Our
experiments are surprising, and suggest that classifiers from the human visual
system can be transferred into a machine with some success. Since these
classifiers seem to capture favorable biases in the human visual system, we
further present an SVM formulation that constrains the orientation of the SVM
hyperplane to agree with the bias from human visual system. Our results suggest
that transferring this human bias into machines may help object recognition
systems generalize across datasets and perform better when very little training
data is available.

本文通过一种新的方法，将人类的视觉分类器的偏见转移到了机器的视觉识别系统中，在很少的训练数据情况下，结果表明，将这种人类的偏见转移给机器可以帮助目标识别系统更好地推广和表现。