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Found 10 papers, 6 papers with code
MedIAnomaly: A comparative study of anomaly detection in medical images
Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns.
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective
To the best of our knowledge, this is the first effort to theoretically clarify the principles and design philosophy of AE for anomaly detection.
Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency
Depth completion, aiming to predict dense depth maps from sparse depth measurements, plays a crucial role in many computer vision related applications.
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Due to the high-cost annotations of abnormal images, most methods utilize only known normal images during training and identify samples deviating from the normal profile as anomalies in the testing phase.
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Subsequently, inter-discrepancy between the two modules, and intra-discrepancy inside the module that is trained on only normal images are designed as anomaly scores to indicate anomalies.
Fast semidefinite programming with feedforward neural networks
Given the optimization constraints as an input, a neural network outputs values for the optimization parameters such that the constraints are satisfied, both for the primal and the dual formulations of the task.
MNN: A Universal and Efficient Inference Engine
Deploying deep learning models on mobile devices draws more and more attention recently.
A neural network oracle for quantum nonlocality problems in networks
As such, the neural network acts as an oracle, demonstrating that a behavior is classical if it can be learned.
Experimental many-pairs nonlocality
Collective measurements on large quantum systems together with a majority voting strategy can lead to a violation of the CHSH Bell inequality.
Quantum Physics
Sparse Coding and Counting for Robust Visual Tracking
In this paper, we propose a novel sparse coding and counting method under Bayesian framwork for visual tracking.
