馒头and花卷

摘要： Neyshabur B., Sedghi H., Zhang C. What is being transferred in transfer learning? arXiv preprint arXiv 2008.11687, 2020. 概 迁移学习到底迁移了什么? 主要内容 T: 普通训练的模 阅读全文

摘要： ROC-wiki 概 AUC常常在文章中作为评价一个分类器优劣的指标, 却总是忘记其原由, 索性记上一笔. TPR, FPR 首先理解TP, FP, FN, TN的概念, 下面是其对应的类型. 这里, P表示正样本, 而N表示负样本, 下表中的行为真实的标签, 列为预测的标签. 预测\真实 P N 阅读全文

摘要： Bai T., Chen J., Zhao J., Wen B., Jiang X., Kot A. Feature Distillation With Guided Adversarial Contrastive Learning. arXiv preprint arXiv 2009.09922, 阅读全文

摘要： Tsiligkaridis T., Roberts J. Second Order Optimization for Adversarial Robustness and Interpretability. arXiv preprint axXiv 2009.04923, 2020. 概 也算是一种 阅读全文

摘要： Pebbles W., Pebbles J., Zhu J., Efros A., Torralba A. The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement. arXiv preprint arXiv 2008.10 阅读全文

摘要： Chen T., Kornblith S., Norouzi M., Hinton G. A Simple Framework for Contrastive Learning of Visual Representations. arXiv: Learning, 2020. @article{ch 阅读全文

摘要： He K., Fan H., Wu Y., Xie S., Girshick R. Momentum Contrast for Unsupervised Visual Representation Learning. arXiv preprint arXiv:1911.05722, 2019. @A 阅读全文

摘要： Den Oord A V, Li Y, Vinyals O, et al. Representation Learning with Contrastive Predictive Coding.[J]. arXiv: Learning, 2018. Henaff O J, Srinivas A, D 阅读全文

摘要： Chopra S, Hadsell R, Lecun Y, et al. Learning a similarity metric discriminatively, with application to face verification[C]. computer vision and patt 阅读全文

摘要： Gutmann M U, Hyvarinen A. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models[C]. international conference on 阅读全文

摘要： Cohen J., Rosenfeld E., Kolter J. Certified Adversarial Robustness via Randomized Smoothing. International Conference on Machine Learning (ICML), 2019 阅读全文

摘要： Lecuyer M, Atlidakis V, Geambasu R, et al. Certified Robustness to Adversarial Examples with Differential Privacy[C]. ieee symposium on security and p 阅读全文

摘要： Sabour S, Frosst N, Hinton G E, et al. Dynamic Routing Between Capsules[C]. neural information processing systems, 2017: 3856-3866. 概 虽然11年就提出了capsule 阅读全文

摘要： Li Y., Xie L., Zhang Y., Zhang R., Wang Y., Tian Q., Defending Adversarial Attacks by Correcting logits[J]. arXiv: Learning, 2019. 概 作者认为, adversarial 阅读全文

摘要： Der Maaten L V, Hinton G E. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008: 2579-2605. 概 t-sne是一个非常经典的可视化方法. 主要内容 我们希望, 将 阅读全文

摘要： Luigi Ambrosio, Giuseppe Da Prato, Andrea Mennucci, An Introduction to Measure Theory and Probability. Chapter 1 Measure spaces Index: ring/algebras P 阅读全文

摘要： Chapter 4 Inverse Function Theorem 这个章节讲得很好, 还引用了庄子秋水中的一段话, 大佬啊. 4.1 The Inverse Function Theorem 映射$F: \mathbb^n \rightarrow \mathbbm$在$p_0$可微, 若存在$D 阅读全文

摘要： JOHNNY HU, BAIRE ONE FUNCTIONS. 一些基本的定义(诸如逐点收敛, 一致收敛$F_{\sigma}$集合等)就不叙述了. 定义 Definition: 令$D\subseteq \mathbb$, 函数$f:D\rightarrow \mathbb\(, 若存在连续函数列 阅读全文

摘要： Hu S, Yu T, Guo C, et al. A New Defense Against Adversarial Images: Turning a Weakness into a Strength[C]. neural information processing systems, 2019 阅读全文

摘要： Mustafa A., Khan S., Hayat M., Goecke R., Shen J., Shao L., Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, arXiv preprin 阅读全文