However, such a global learning rate schedule does not take into account the structural characteristics of neural networks (NNs).

We start by providing motivation for the unconstrained Jacobians problem introduced in the main text. We will continue our proof using contradiction. Figure 1: Fully-connected ResNet34 (Type 1 model) trained on MNIST.Figure 2: Fully-connected ResNet34 (Type 1 model) trained on FashionMNIST. Figure 10: Fully-connected ResNet34 (Type 1 model) trained on MNIST. Figure 24: Fully-connected ResNet34 (Type 1 model) trained on MNIST.

If Eq.(3) holds, then null Lemma 2. If Eq.(3) holds, then Υ For independent and identically distributed (i.i.d.) random vector Let g ([x, y,z ]) = y/x z. Proposition 1. subtracting a fixed constant from the classification logits leads to the same consistency OOD datasets to verify the effectiveness of the proposed two regularization terms. The result is the average value across all OOD datasets.ablation Fig.4 shows the distribution of log-likelihood values

Previously, image interpretation in radiology relied heavily on manual methods. However, manual classification of brain tumor medical images is time-consuming and labor-intensive. Even with shallow convolutional neural network models, the accuracy is not ideal. To improve the efficiency and accuracy of brain tumor image classification, this paper proposes a brain tumor classification model based on an improved ResNet34 network. This model uses the ResNet34 residual network as the backbone network and incorporates multi-scale feature extraction. It uses a multi-scale input module as the first layer of the ResNet34 network and an Inception v2 module as the residual downsampling layer. Furthermore, a channel attention mechanism module assigns different weights to different channels of the image from a channel domain perspective, obtaining more important feature information. The results after a five-fold crossover experiment show that the average classification accuracy of the improved network model is approximately 98.8%, which is not only 1% higher than ResNet34, but also only 80% of the number of parameters of the original model. Therefore, the improved network model not only improves accuracy but also reduces clutter, achieving a classification effect with fewer parameters and higher accuracy.

More details are reported in Table 1. In Table 3, we report the statistics of the used datasets in our submission. For all the datasets, we train a single model instead of performing category-specific training. Note that, 3D-FUTURE here contains more fine-grained 3D CAD models. We make qualitative comparisons with two widely studied retrieval solutions, including 2.5D-Sketch The results are shown in Figure 2. We can AMV -ML for fair comparisons, thus can also obtain reasonable retrieval results.

The supplementary material consists of the following. Results are reported in Tables 2 and 3 Discussion on Limitations and Societal Impacts. The architecture of the network is similar to [2]. We perform all our experiments on Nivida Titan X GPU . We used the data splits released by [1] for experimentation.