In recent studies, several asymptotic upper bounds on generalization errors on deep neural networks (DNNs) are theoretically derived. These bounds are functions of several norms of weights of the DNNs, such as the Frobenius and spectral norms, and they are computed for weights grouped according to either input and output channels of the DNNs. In this work, we conjecture that if we can impose multiple constraints on weights of DNNs to upper bound the norms of the weights, and train the DNNs with these weights, then we can attain empirical generalization errors closer to the derived theoretical bounds, and improve accuracy of the DNNs.
To this end, we pose two problems. First, we aim to obtain weights whose different norms are all upper bounded by a constant number, e.g. 1.0. To achieve these bounds, we propose a two-stage renormalization procedure; (i) normalization of weights according to different norms used in the bounds, and (ii) reparameterization of the normalized weights to set a constant and finite upper bound of their norms. In the second problem, we consider training DNNs with these renormalized weights. To this end, we first propose a strategy to construct joint spaces (manifolds) of weights according to different constraints in DNNs. Next, we propose a fine-grained SGD algorithm (FG-SGD) for optimization on the weight manifolds to train DNNs with assurance of convergence to minima. Experimental results show that image classification accuracy of baseline DNNs can be boosted using FG-SGD on collections of manifolds identified by multiple constraints.
A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers
Australian National University（澳大利亚国立大学）；Data61-Commonwealth Scientific Industrial Research Organization（Data61-联邦科学与工业研究组织）；Commonwealth Scientific Industrial Research Organization（联邦科学与工业研究组织）；The University of Technology Sydney（悉尼科技大学）
Relational Proxies: Emergent Relationships as Fine-Grained Discriminators
University of Exeter（埃克塞特大学）；University of Tübingen（图宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Surrey（萨里大学）
Fine-Grained Image Analysis with Deep Learning: A Survey
Nanjing University of Science and Technology（南京理工大学）；University of Surrey（萨里大学）；University of Edinburgh（爱丁堡大学）；Nanjing University（南京大学）；Peking University（北京大学）；University of Copenhagen（哥本哈根大学）；Pioneer Centre for AI（先锋人工智能中心）
Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling
Bayer AG（拜耳股份公司）
本文介绍了对比重建，ConRec -一种自监督学习算法，通过联合优化对比和自重建损失来获得图像表示。我们展示了最先进的对比学习方法（例如Simplified）在其表示中捕获细粒度视觉特征方面存在缺陷。ConRec通过添加（1）自我重建任务和（2）对比学习任务中的注意力机制扩展了Simplified框架。这是通过应用具有两个头的简单编码器-解码器架构来实现的。我们表明，这两个扩展有助于改善图像的矢量表示与细粒度的视觉特征。结合这些概念，ConRec在细粒度分类数据集上的性能优于Simplified和Simplified with Attention-Pooling。
Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization
School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）；Key Laboratory of Data Intelligence and Management, Beihang University, Ministry of Industry and Information Technology（信息产业部北京航空航天大学数据智能与管理重点实验室）
