Semantic segmentation models based on convolutional neural networks have
recently displayed remarkable performance for a multitude of applications.
However, these models typically do not generalize well when applied on new
domains, especially when going from synthetic to real data. In this paper we
address the problem of unsupervised domain adaptation (UDA), which attempts to
train on labelled data from one domain (source domain), and simultaneously
learn from unlabelled data in the domain of interest (target domain). Existing
methods have seen success by training on pseudo-labels for these unlabelled
images. Multiple techniques have been proposed to mitigate low-quality
pseudo-labels arising from the domain shift, with varying degrees of success.
We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes
images from the two domains along with the corresponding labels and
pseudo-labels. These mixed samples are then trained on, in addition to the
labelled data itself. We demonstrate the effectiveness of our solution by
achieving state-of-the-art results for GTA5 to Cityscapes, a common
synthetic-to-real semantic segmentation benchmark for UDA.

使用 DACs 方法，混合源领域的标签数据和目标领域的伪标签数据进行训练可以有效地解决基于卷积神经网络的语义分割模型在新领域上通用性不佳的问题。

DACS: 基于跨域混合采样的领域自适应

DACS: Domain Adaptation via Cross-domain Mixed Sampling

Due to the heavy reliance of millimeter-wave (mmWave) wireless systems on
directional links, beamforming (BF) with high-dimensional arrays is essential
for cellular systems in these frequencies. How to perform the array processing
in a power efficient manner is a fundamental challenge. Analog and hybrid BF
require fewer analog-to-digital and digital-to-analog converters (ADCs and
DACs), but can only communicate in a small number of directions at a time,
limiting directional search, spatial multiplexing and control signaling.
Digital BF enables flexible spatial processing, but must be operated at a low
quantization resolution to stay within reasonable power levels. This decrease
in quantizer resolution introduces noise in the received signal and degrades
the quality of the transmitted signal. To assess the effect of low-resolution
quantization on cellular system, we present a simple additive white Gaussian
noise (AWGN) model for quantization noise. Simulations with this model reveal
that at moderate resolutions (3-4 bits per ADC), there is negligible loss in
downlink cellular capacity from quantization. In essence, the low-resolution
ADCs limit the high SNR, where cellular systems typically do not operate. For
the transmitter, it is shown that DACs with 4 or more bits of resolution do not
violate the adjacent carrier leakage limit set by 3-rd Generation Partnership
Project (3GPP) New Radio (NR) standards for cellular operations. Further, this
work studies the effect of low resolution quantization on the error vector
magnitude (EVM) of the transmitted signal.In fact, our findings suggests that
low-resolution fully digital BF architectures can be a power efficient
alternative to analog or hybrid beamforming for both transmitters and receivers
at millimeter wave.

介绍了在毫米波频率下进行天线阵列处理的基本挑战，探讨了采用低分辨率量化实现数字波束成形架构的功率效率以及它们在下行蜂窝容量和误差矢量幅度方面的影响。