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Found 15 papers, 4 papers with code
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications.
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments
Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention.
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation
In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of segmentation models against natural degradation factors (e. g., rain streaks) or artificially attack factors (e. g., adversarial attack).
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding.
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity
Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning fields.
Efficient Cavity Searching for Gene Network of Influenza A Virus
High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution and are key signals that indicate viral cross-species infection and cause pandemics.
LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation
This paper presents a lightweight, efficient calibration neural network model for denoising low-cost microelectromechanical system (MEMS) gyroscope and estimating the attitude of a robot in real-time.
Triple-level Model Inferred Collaborative Network Architecture for Video Deraining
In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances.
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond
In particular, by introducing an auxiliary as initialization to guide the optimization dynamics and designing a pessimistic trajectory truncation operation, we construct a reliable approximate version of the original BLO in the absence of LLC hypothesis.
Field-induced intermediate ordered phase and anisotropic interlayer interactions in $α$-RuCl$_3$
In $\alpha$-RuCl$_3$, an external magnetic field applied within the honeycomb plane can induce a transition from a magnetically ordered state to a disordered state that is potentially related to the Kitaev quantum spin liquid.
Strongly Correlated Electrons Materials Science
BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning
learning to learn) has recently emerged as a promising paradigm for a variety of applications.
Communication-Censored Linearized ADMM for Decentralized Consensus Optimization
COLA is proven to be convergent when the local cost functions have Lipschitz continuous gradients and the censoring threshold is summable.
Discovering Features in Sr$_{14}$Cu$_{24}$O$_{41}$ Neutron Single Crystal Diffraction Data by Cluster Analysis
To address the SMC'18 data challenge, "Discovering Features in Sr$_{14}$Cu$_{24}$O$_{41}$", we have used the clustering algorithm "DBSCAN" to separate the diffuse scattering features from the Bragg peaks, which takes into account both spatial and photometric information in the dataset during in the clustering process.
Computer Vision-aided Atom Tracking in STEM Imaging
To address the SMC'17 data challenge -- "Data mining atomically resolved images for material properties", we first used the classic "blob detection" algorithms developed in computer vision to identify all atom centers in each STEM image frame.
Volumetric Data Exploration with Machine Learning-Aided Visualization in Neutron Science
Recent advancements in neutron and X-ray sources, instrumentation and data collection modes have significantly increased the experimental data size (which could easily contain 10$^{8}$ -- 10$^{10}$ data points), so that conventional volumetric visualization approaches become inefficient for both still imaging and interactive OpenGL rendition in a 3D setting.
