Results 71 to 80 of about 588,587 (281)

Generative Cooperative Net for Image Generation and Data Augmentation

, 2018
How to build a good model for image generation given an abstract concept is a fundamental problem in computer vision. In this paper, we explore a generative model for the task of generating unseen images with desired features.
Qin, Zengchang, Wan, Tao, Xu, Qiangeng
core   +1 more source

Hyperspectral Data Augmentation

, 2019
Submitted to IEEE Geoscience and Remote Sensing ...
Nalepa, Jakub, Myller, Michal, Kawulok, Michal   +2 more
openaire   +2 more sources

Differentiable Data Augmentation with Kornia

, 2020
In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors. This module leverages differentiable computer vision solutions from Kornia, with an aim of integrating data augmentation (DA) pipelines and strategies to existing PyTorch components (e.g.
Shi, Jian, Riba Pi, Edgar, Mishkin, Dmytro, Moreno-Noguer, Francesc, Nicolaou, Anguelos   +4 more
openaire   +4 more sources

The cooperative regulation of miR‐221 by APE1 and AUF1 impacts p27Kip1 defining a miR signature relevant for cervical cancer

FEBS Open Bio, EarlyView.
A regulatory axis involving APE1, AUF1, and miR‐221 is proposed. Pri‐miR‐221 is processed by DROSHA and DICER to generate mature miR‐221, which targets p27Kip1 mRNA. APE1 and AUF1 compete for pre‐miR‐221 binding. Reduced APE1/AUF1 levels impair miR‐221 biogenesis, decrease p27Kip1 mRNA degradation, and promote cell cycle progression, chemoresistance ...
Matilde Clarissa Malfatti, Nicolò Gualandi, Gianluca Tell, Giulia Antoniali   +3 more
wiley   +1 more source

Remote Sensing Data Augmentation Through Adversarial Training

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
The lack of remote sensing images and poor quality limit the performance improvement of follow-up research such as remote sensing interpretation. In this article, a generative adversarial network (GAN) is proposed for data augmentation of remote sensing ...
Ning Lv, Hongxiang Ma, Chen Chen, Qingqi Pei, Yang Zhou, Fenglin Xiao, Ji Li   +6 more
doaj   +1 more source

Gibbs Max-margin Topic Models with Data Augmentation [PDF]

, 2013
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic ...
Chen, Ning, Perkins, Hugh, Zhang, Bo, Zhu, Jun   +3 more
core  

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source

Data Augmentation-Based Photovoltaic Power Prediction

Energies
In recent years, as the grid-connected installed capacity of photovoltaic (PV) power generation has increased by leaps and bounds, it has assumed considerable importance in predicting PV power output.
Xifeng Wang, Yijun Shen, Haiyu Song, Shichao Liu   +3 more
doaj   +1 more source

HoloDetect: Few-Shot Learning for Error Detection

, 2019
We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detection models that require minimal human involvement. Our framework consists of
Bengio Yoshua, Elmagarmid Ahmed K., Globerson Amir, Goodfellow Ian, Guo Chuan, Hinton G. E., Rahm Erhard, Ratcliff John W., Zhang Yu, Zhu Xiaojin   +9 more
core   +1 more source

Evolutionarily divergent DUF4465 domains have a common vitamin B12‐binding function

FEBS Open Bio, EarlyView.
We show that DUF4465 family proteins, widespread across bacteria from gut microbiomes, hydrothermal vents, and soil, share a common vitamin B12‐binding function. These augmented β‐jellyroll proteins bind vitamin B12 via extended loops. Our findings establish sequence‐diverse DUF4465 proteins as a widespread class of B12‐binding proteins, highlighting ...
Charlea Clarke, Michal Banasik, Rokas Juodeikis, Martin J. Warren, Richard W. Pickersgill   +4 more
wiley   +1 more source