Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro. Exploring Generalization in Deep Learning. arXiv, jun 2017.Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nati Srebro. Exploring generalization in deep learning. In Advances in Neural Information Processing...
showing the inefficiencies in training deep SNN networks. Finally, we apply the learning rules found in our experiments to theAnt-v4benchmark in MuJoCo37showing an increase in performance of 4.4×compared to the state-of-the-art spiking network proposed for the same task5. In summary, our wor...
making it effective in handling noise and reducing power consumption. HOSIB further advances SNN training by incorporating second-order (SOIB) and third-order (TOIB) strategies, achieving better generalization, robustness, and power efficiency, particularly in deeper networks like VGG9 and ResNet18....
It has been shown that this iterative training of weak learners can lower the generalization error and collectively result in a strong classifier. The final classification output is a linear combination of the weighted weak learners: $$\mathrm{Y }=\mathrm{ sign}(\sum_{t=1}^{T}{\alpha }_...
Generalization and Transfer Learning: The brain excels at generalizing knowledge from one context to another. For instance, learning to ride a bicycle can make it easier to learn to ride a motorcycle. DRL is beginning to achieve similar feats, with agents that can transfer knowledge across differ...
(), relying on an optimal separating hyperplane to partition data into distinct classes. By maximizing the margin and ensuring the largest possible distance between data points on either side of the hyperplane, SVMs reduce the upper bound of generalization error. Notably, the model’s complexity ...
Fs-ban: Born-again networks for domain generalization few-shot classification. IEEE Transactions on Image Processing, 2023. 2 [77] Yunqing Zhao, Henghui Ding, Houjing Huang, and Ngai- Man Cheung. A closer look at few-shot image generation. In CVPR, 2022. 2...
uate the robustness and generalization capability of EVA trained with an image size of 3362 on 6 different ImageNet- 1K validation set variants. In Table 5, we compare EVA with some top open-sourced models collected by the timm library [108]. Following the eval...
Lu, W., Xixu, H., Wang, J., Xie, X.: FedCLIP: fast generalization and personalization for CLIP in federated learning. In: ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models (2023) Google Scholar Malo, P., Sinha, A., Korhonen, P., Wallenius, J., Ta...
In recent years, deep learning-based approaches have achieved promising success on video deblurring task. However, the models trained on existing synthetic datasets still suffer from generalization problems over real-world blurry scenarios with undesired artifacts. The factors accounting for the failure ...