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Found 13 papers, 8 papers with code
Adaptive Approximate Implicitization of Planar Parametric Curves via Weak Gradient Constraints
Converting a parametric curve into the implicit form, which is called implicitization, has always been a popular but challenging problem in geometric modeling and related applications.
Weld Defect Segmentation in Xray Image with Boundary Label Smoothing
Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries.
Data-Efficient Graph Grammar Learning for Molecular Generation
This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data.
Polygrammar: Grammar for Digital Polymer Representation and Generation
Here, we present a parametric, context-sensitive grammar designed specifically for the representation and generation of polymers.
Towards Evaluating and Training Verifiably Robust Neural Networks
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks.
Texture Memory-Augmented Deep Patch-Based Image Inpainting
By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks.
Online Hyper-parameter Learning for Auto-Augmentation Strategy
Data augmentation is critical to the success of modern deep learning techniques.
AM-LFS: AutoML for Loss Function Search
The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation.
See the World through Network Cameras
This paper describes the real-time data available from worldwide network cameras and potential applications.
IRLAS: Inverse Reinforcement Learning for Architecture Search
Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt).
Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking
In this paper, we propose a dual-agent deep reinforcement learning (DADRL) method for deformable face tracking, which generates bounding boxes and detects facial landmarks interactively from face videos.
