Prior neural architecture search (NAS) for adversarial robustness works have
discovered that a lightweight and adversarially robust neural network
architecture could exist in a non-robust large teacher network, generally
disclosed by heuristic rules through statistical analysis and neural
architecture search, generally disclosed by heuristic rules from neural
architecture search. However, heuristic methods cannot uniformly handle
different adversarial attacks and "teacher" network capacity. To solve this
challenge, we propose a Reinforced Compressive Neural Architecture Search
(RC-NAS) for Versatile Adversarial Robustness. Specifically, we define task
settings that compose datasets, adversarial attacks, and teacher network
information. Given diverse tasks, we conduct a novel dual-level training
paradigm that consists of a meta-training and a fine-tuning phase to
effectively expose the RL agent to diverse attack scenarios (in meta-training),
and making it adapt quickly to locate a sub-network (in fine-tuning) for any
previously unseen scenarios. Experiments show that our framework could achieve
adaptive compression towards different initial teacher networks, datasets, and
adversarial attacks, resulting in more lightweight and adversarially robust
architectures.

通过双层训练范式和适应多样攻击情境的框架，我们提出了一种适应性压缩神经构架搜索方法，使得神经网络具备更轻量级和更具鲁棒性。

增强型压缩神经架构搜索对于多用途对抗鲁棒性的研究

Reinforced Compressive Neural Architecture Search for Versatile  Adversarial Robustness

Face parsing is defined as the per-pixel labeling of images containing human
faces. The labels are defined to identify key facial regions like eyes, lips,
nose, hair, etc. In this work, we make use of the structural consistency of the
human face to propose a lightweight face-parsing method using a Local Implicit
Function network, FP-LIIF. We propose a simple architecture having a
convolutional encoder and a pixel MLP decoder that uses 1/26th number of
parameters compared to the state-of-the-art models and yet matches or
outperforms state-of-the-art models on multiple datasets, like CelebAMask-HQ
and LaPa. We do not use any pretraining, and compared to other works, our
network can also generate segmentation at different resolutions without any
changes in the input resolution. This work enables the use of facial
segmentation on low-compute or low-bandwidth devices because of its higher FPS
and smaller model size.

通过提出的本地隐式函数网络，我们在不预训练的情况下，提出了一种轻量级的人脸分割方法 FP-LIIF，使用卷积编码器和像素 MLP 解码器，并且相对于现有方法，我们的网络可以在不更改输入分辨率的情况下生成不同分辨率的分割，且模型尺寸小且速度更快，适用于低计算资源或低带宽设备。

适用于人脸分割的参数高效的本地隐式图像函数网络

Parameter Efficient Local Implicit Image Function Network for Face  Segmentation

Active speaker detection is a challenging task in audio-visual scenario
understanding, which aims to detect who is speaking in one or more speakers
scenarios. This task has received extensive attention as it is crucial in
applications such as speaker diarization, speaker tracking, and automatic video
editing. The existing studies try to improve performance by inputting multiple
candidate information and designing complex models. Although these methods
achieved outstanding performance, their high consumption of memory and
computational power make them difficult to be applied in resource-limited
scenarios. Therefore, we construct a lightweight active speaker detection
architecture by reducing input candidates, splitting 2D and 3D convolutions for
audio-visual feature extraction, and applying gated recurrent unit (GRU) with
low computational complexity for cross-modal modeling. Experimental results on
the AVA-ActiveSpeaker dataset show that our framework achieves competitive mAP
performance (94.1% vs. 94.2%), while the resource costs are significantly lower
than the state-of-the-art method, especially in model parameters (1.0M vs.
22.5M, about 23x) and FLOPs (0.6G vs. 2.6G, about 4x). In addition, our
framework also performs well on the Columbia dataset showing good robustness.
The code and model weights are available at
this https URL

提出了一种轻量级的主动说话人检测框架，通过减少输入候选信息、分离 2D 和 3D 卷积进行音视频特征提取、并应用门控循环单元（GRU）进行跨模态建模，可以在计算、资源消耗上比现有方法表现更优。

一个轻量级的主动说话人检测模型

A Light Weight Model for Active Speaker Detection

One of the most pressing challenges prevalent in the steel manufacturing
industry is the identification of surface defects. Early identification of
casting defects can help boost performance, including streamlining production
processes. Though, deep learning models have helped bridge this gap and
automate most of these processes, there is a dire need to come up with
lightweight models that can be deployed easily with faster inference times.
This research proposes a lightweight architecture that is efficient in terms of
accuracy and inference time compared with sophisticated pre-trained CNN
architectures like MobileNet, Inception, and ResNet, including vision
transformers. Methodologies to minimize computational requirements such as
depth-wise separable convolution and global average pooling (GAP) layer,
including techniques that improve architectural efficiencies and augmentations,
have been experimented. Our results indicate that a custom model of 590K
parameters with depth-wise separable convolutions outperformed pretrained
architectures such as Resnet and Vision transformers in terms of accuracy
(81.87%) and comfortably outdid architectures such as Resnet, Inception, and
Vision transformers in terms of faster inference times (12 ms). Blurpool fared
outperformed other techniques, with an accuracy of 83.98%. Augmentations had a
paradoxical effect on the model performance. No direct correlation between
depth-wise and 3x3 convolutions on inference time, they, however, they played a
direct role in improving model efficiency by enabling the networks to go deeper
and by decreasing the number of trainable parameters. Our work sheds light on
the fact that custom networks with efficient architectures and faster inference
times can be built without the need of relying on pre-trained architectures.

提出了一种轻量级的深度学习模型用于在钢铁制造行业中识别表面缺陷，与 MobileNet、Inception、ResNet 和 Vision transformers 等预训练卷积神经网络相比，该新模型具有更高的准确性和更快的推理速度，并采用了深可分离卷积，全局平均池化层和改进的架构高效性增强技术。