[65] demonstrated the effective capacity of several successful neural network architectures is large enough to memorize random labels, the community sees a prosperity of many discussions about this apparent "paradox" [61, 15, 17, 15, 11]. Arpit et ...
This process helps in reducing the dimensionality of feature maps, thereby simplifying the computations required and improving the efficiency of the network. In this blog, we will explore what the CNN pooling layer is and discuss the various types of pooling used in CNN architectures. What is ...
implemented several deep learning architectures that far surpass the traditional denoising filters. In this blog, I will explain my approach step-by-step as a case study, starting from the problem formulation to implementing the state-of-the-art deep learning models, and then finally see the ...
7. Evaluating representations by the complexity of learning low-loss predictors. (from Kyunghyun Cho) 8. Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification. (from Yang Gao) 9. Decoupling Representation Learning from Reinforcement Learning. (from Pieter Abbeel) 10....
Experience in the field suggests that larger models perform better5,6,7, encouraging training of larger and larger networks with state-of-the-art architectures reaching hundreds of billions of parameters7. These networks work in an overparameterized regime3,5 with much more parameters than training...
Our results show that an ablation is a powerful tool as it allows to understand 1) in which layers various countermeasures are processed, 2) whether it is possible to use smaller neural network architectures without performance penalties, and 3) how to redesign neural networks to improve the ...
(2019). Data augmentation is more important than model architectures for retinal vessel segmentation. In: Proceedings of the 2019 International Conference on Intelligent Medicine and Health, pp. 48–52. Zhang, C., Tavanapong, W., Wong, J., de Groen, P.C., Oh, J (2017) Real data ...
However, performance gains associ- ated with recurrent architectures have previously been shown only for small-scale visual tasks [26–28] or using specialised forms of recurrence [29]. Here we investigated whether rCNNs can outperform feedforward control models matched in their ...
This is regarded as one of the best vision model architectures created to date. The most distinctive feature of VGG16 is that it emphasizes having convolution layers of 3 × 3 filters with a stride of 1 and always utilizes the same padding and MaxPool layer of 2 × 2 filters with a ...