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Found 9 papers, 2 papers with code
Novelty Detection via Contrastive Learning with Negative Data Augmentation
Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
Contrastive Learning for Compact Single Image Dehazing
In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
Strain effects on monolayer MoSi2N4: ideal strength and failure mechanism
Interestingly, the critical strain and failure mechanism of zigzag direction in MoSi2N4 are almost the same as those of armchair direction in MoS2, while the critical strain and failure mechanism of armchair direction for MoSi2N4 are similar to the ones of zigzag direction for MoS2.
Mesoscale and Nanoscale Physics
Trace anomaly contribution to hydrogen atom mass
We compute trace anomaly contribution to hydrogen atom mass, which turns out to be related to the part of the Lamb shift.
High Energy Physics - Phenomenology
New opportunities at the photon energy frontier
Ultra-peripheral collisions (UPCs) involving heavy ions and protons are the energy frontier for photon-mediated interactions.
High Energy Physics - Phenomenology High Energy Physics - Experiment Nuclear Experiment
Multistage Model for Robust Face Alignment Using Deep Neural Networks
First, a spatial transformer - generative adversarial network which consists of convolutional layers and residual units is utilized to solve the initialization issues caused by face detectors, such as rotation and scale variations, to obtain improved face bounding boxes for face alignment.
Perceptual Visual Interactive Learning
Supervised learning methods are widely used in machine learning.
Image reconstruction from limited range projections using orthogonal moments
A set of orthonormal polynomials is proposed for image reconstruction from projection data.
Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction
Here we present a new supervised generative stochastic network (GSN) based method to predict local secondary structure with deep hierarchical representations.
Protein Secondary Structure Prediction
Protein Structure Prediction