Facial Expression Recognition (FER) is a machine learning problem that deals
with recognizing human facial expressions. While existing work has achieved
performance improvements in recent years, FER in the wild and under challenging
conditions remains a challenge. In this paper, a lightweight patch and
attention network based on MobileNetV1, referred to as PAtt-Lite, is proposed
to improve FER performance under challenging conditions. A truncated
ImageNet-pre-trained MobileNetV1 is utilized as the backbone feature extractor
of the proposed method. In place of the truncated layers is a patch extraction
block that is proposed for extracting significant local facial features to
enhance the representation from MobileNetV1, especially under challenging
conditions. An attention classifier is also proposed to improve the learning of
these patched feature maps from the extremely lightweight feature extractor.
The experimental results on public benchmark databases proved the effectiveness
of the proposed method. PAtt-Lite achieved state-of-the-art results on CK+,
RAF-DB, FER2013, FERPlus, and the challenging conditions subsets for RAF-DB and
FERPlus. The source code for the proposed method will be available at
this https URL

本文提出了一种基于 MobileNetV1 的轻量级 patch 和 attention 网络，旨在改善面部表情识别（FER）在复杂环境下的性能，实验结果表明，该方法在公共基准数据库上取得了最先进的结果。

PAtt-Lite：挑战性面部表情识别的轻量级 MobileNet 补丁与注意力模型

PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging  Facial Expression Recognition

The success of neural networks has driven a shift in focus from feature
engineering to architecture engineering. However, successful networks today are
constructed using a small and manually defined set of building blocks. Even in
methods of neural architecture search (NAS) the network connectivity patterns
are largely constrained. In this work we propose a method for discovering
neural wirings. We relax the typical notion of layers and instead enable
channels to form connections independent of each other. This allows for a much
larger space of possible networks. The wiring of our network is not fixed
during training -- as we learn the network parameters we also learn the
structure itself. Our experiments demonstrate that our learned connectivity
outperforms hand engineered and randomly wired networks. By learning the
connectivity of MobileNetV1we boost the ImageNet accuracy by 10% at ~41M FLOPs.
Moreover, we show that our method generalizes to recurrent and continuous time
networks. Our work may also be regarded as unifying core aspects of the neural
architecture search problem with sparse neural network learning. As NAS becomes
more fine grained, finding a good architecture is akin to finding a sparse
subnetwork of the complete graph. Accordingly, DNW provides an effective
mechanism for discovering sparse subnetworks of predefined architectures in a
single training run. Though we only ever use a small percentage of the weights
during the forward pass, we still play the so-called initialization lottery
with a combinatorial number of subnetworks. Code and pretrained models are
available at this https URL while additional visualizations may
be found at this https URL

本文提出一种新的神经网络结构发现方法，即通过松弛层的概念，通过管道形式构建独立互连通道的方式提出了一种新的网络架构组合方式，可以学习具有比手动设计和随机网络更高的准确性的神经网络结构，并可用于移动定时网络和稀疏子网络发现。

发现神经连线

Discovering Neural Wirings

As deep learning (DL) is being rapidly pushed to edge computing, researchers
invented various ways to make inference computation more efficient on
mobile/IoT devices, such as network pruning, parameter compression, and etc.
Quantization, as one of the key approaches, can effectively offload GPU, and
make it possible to deploy DL on fixed-point pipeline. Unfortunately, not all
existing networks design are friendly to quantization. For example, the popular
lightweight MobileNetV1, while it successfully reduces parameter size and
computation latency with separable convolution, our experiment shows its
quantized models have large accuracy gap against its float point models. To
resolve this, we analyzed the root cause of quantization loss and proposed a
quantization-friendly separable convolution architecture. By evaluating the
image classification task on ImageNet2012 dataset, our modified MobileNetV1
model can archive 8-bit inference top-1 accuracy in 68.03%, almost closed the
gap to the float pipeline.

本文分析了 MobileNetV1 量化造成的精度损失问题，并提出了一种友好于量化的可分离卷积架构，经 ImageNet2012 数据集测试，我们改进后的 MobileNetV1 模型能够以 8 位推理精度达到 68.03%，几乎与其浮点精度模型无异。