Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by multi-scale feature fusion. In this paper, a new paradigm for semantic …
High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images. However, …
We propose a boosting algorithm for regression problems called boosted histogram transform for regression (BHTR) based on histogram transforms composed of random rotations, stretchings, and translations.
We explore the relationship between the adversarial robustness and numerical stability. Furthermore, we propose IE-Skips, which is a modification of the vaniila skip connections for Residual Network Families inspired by the implicit Euler method and we also theoretically and exmperimently prove the advantages of our structure under adversarial attacks.
We propose a novel multi-layer DNN structure termed MCN, which can approximate some class of continuous functions arbitrarily well even with highly sparse connection. We prove that the global minima of an $l$-layer MCN may be outperformed, at least can be attained, by simply increasing the network depth. More importantly, MCN could be easily appended to any of the many existing DNN and the augmented DNN will share the same property of MCN.
Robust loss functions are essential for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels. It has been shown that the commonly used Cross Entropy (CE) loss is not robust to noisy labels. Whilst new loss …
We employ PDOs as steerable filters to design a system equivariant to E(n). We use numerical schemes to discretize this system and obtain PDO-eConvs, which achieve a quadratic order equivariance approximation in the discrete domain.
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. A range of defense techniques have been proposed to improve DNN robustness to adversarial examples, among which adversarial training has been …
Skip connections are an essential component of current state-of-the-art deep neural networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their huge success in building deeper and more powerful DNNs, we identify a surprising …
The convolution operation suffers from a limited receptive filed, while global modeling is fundamental to dense prediction tasks, such as semantic segmentation. In this paper, we apply graph convolution into the semantic segmentation task and propose …