NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

InTrain: 面向零成本神经架构搜索的内在可训练性

InTrain: Intrinsic Trainability for Zero-Cost Neural Architecture Search

提出统一理论代理InTrain，通过几何容量和优化韧性两个协同成分形式化架构的可训练性，在NAS基准上达到与集成方法相当的排序相关性。

Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification

Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model. Specially, we use \emph{neural architecture search} (NAS) to automatically search 3D network architectures with excellent accuracy/speed trade-off. Besides, we use the convolutional block attenti…

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called 'adversarial subspaces') in which adversarial examples lie. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, ba…

A Semi-Supervised Assessor of Neural Architectures

Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficiently value an intermediate neural architecture. But for the training of this predictor, a number of neural architectures and their corresponding real performance often have to be collected. In contrast with classical performance predictor optimized in a fully supervised way, this paper suggests a semi-supervised assessor of neural architectures. We employ an auto-encoder to discover meaningful representations of neural…

AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture

Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including both cell-based and node-based approaches, the search process is time consuming and the performance of found architecture may be sub-optimal. To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search(NAS) for searching efficient building block…

神经架构搜索作为多目标优化基准：问题表述与性能评估

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

本文将NAS任务形式化为多目标优化问题，提出无需GPU即可高效运行的EvoXBench基准生成流水线，并构建覆盖多数据集、搜索空间和硬件设备的测试套件以评估EMO算法。

When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustness of deep networks via specialized learning algorithms and loss functions. In this work, we take an architectural perspective and investigate the patterns of network architectures that are resilient to adversarial attacks. To obtain the large number of networks needed for this study, we adopt one-shot neural architecture search, training a large network for once and then finetuning the sub-networks sampled therefrom…

GLID：门控局部内在维度修复面部伪造检测器的盲点

GLID: Gated Local Intrinsic Dimension Repairs the Blind Spots of Face-Forgery Detectors

研究针对微调基础模型面部伪造检测器对训练外生成器家族有盲点的问题，提出GLID检测器，通过估计图像补丁令牌局部内在维度，经置信门进入微调检测器，在基准测试中表现出色，揭示相关经验法则并降低准确率跨种子传播。