馒头and花卷

摘要： Yam J. Y. F. and Chow T. W. S. Feedforward networks training speed enhancement by optimal initialization of the synaptic coefficients. 概 和 here 超级像的一个 阅读全文

摘要： Xu H., Liu X., Wang W., Jain A. K. Tang J., Ding W., Wu Z. and Liu Z. Towards the memorization effect of neural networks in adversarial training. In I 阅读全文

摘要： Ni S., Li J. and Kao H. DropAttack: a masked weight adversarial training method to improve generalization of neural networks. In International Confere 阅读全文

摘要： Shimodaira H. and Ltd N. M. C. A weight value initialization method for improving learning performance of the backpropagation algorithm in neural netw 阅读全文

摘要： Wessels L. F. A. and Barnard E. Avoiding False local minima by proper initialization of connections. In IEEE Transactions on Neural Networks, 1992. 概 阅读全文

摘要： Nguyen D. and Widrow B. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In International J 阅读全文

摘要： Ho J., Jain A. and Abbeel P. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NIPS), 2020. [Page E. App 阅读全文

摘要： Song Y. and Ermon S. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems (NIPS) 阅读全文

摘要： motivation 试一下faster rcnn的代码, 主要想看看backbone训练一部分好呢还是全部都训练好呢? 实验设置 Attribute Value backbone resnet50 batch_size 2 batch_size_per_image 256 benchmark Tr 阅读全文

摘要： Hendrycks D., Basart S., Mu N., Kadavath S., Wang F., Dorundo E., Desai R., Zhu T., Parajuli S., Guo M., Song D., Steinhardt J. Gilmer J. The many fac 阅读全文

摘要： Chen E. and Lee C. LTD: Low temperature distillation for robust adversarial training. arXiv preprint arXiv:2111.02331, 2021. 概 本文利用distillation来提高网络鲁棒 阅读全文

摘要： Girshick R., Donahue J., Darrel T. and Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation Tech report. In IEEE 阅读全文

摘要： Wang Z., Zhang W., Liu N. and Wang J. Scalable rule-based representation learning for interpretable classification. In Advances in Neural Information 阅读全文

摘要： Croce F. and Hein M. Mind the box: \(\ell_1\)-APGD for sparse adversarial attacks on image classifiers. In International Conference on Machine Learnin 阅读全文

摘要： Huster T., Chiang C. J. and Chadha R. Limitations of the lipschitz constant as a defense against adversarial examples. In European Conference on Machi 阅读全文

摘要： Huang H., Wang Y., Erfani S., Gu Q., Bailey J. and Ma X. Exploring architectural ingredients of adversarially robust deep neural networks. In Advances 阅读全文

摘要： Rade R. and Moosavi-Dezfooli S. Helper-based adversarial training: reducing excessive margin to achieve a better accuracy vs. robustness trade-off. In 阅读全文

摘要： Zhang J., Xu X., Han B., Niu G., Cui L., Sugiyama M., Kankanhalli M. Attacks which do not kill training make adversarial learning stronger. In Interna 阅读全文

摘要： Andriushchenko M. and Flammarion N. Understanding and improving fast adversarial training. In Advances in Neural Information Processing Systems (NIPS) 阅读全文

摘要： Mildenhall B., Srinivasan P. P., Tancik M., Barron J. T., Ramamoorthi R. and Ng R. NeRF: representing scenes as neural radiance fields for view synthe 阅读全文