Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data Automatic image-based disease severity estimation generally uses discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult due to the images with ambiguous severity. An easier alternative is to use relativ...
This paper proposes a deep Bayesian active-learning-to-rank, which trains a Bayesian convolutional neural network while automatically selecting appropriate pairs for relative annotation. We confirmed the efficiency of the proposed method through experiments on endoscopic images of ulcerative colitis. In ...
Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noi...
Variants of active learning depend on the way the new samples are selected. One method of choice is selection which maximises the diversity of the training set37. Gal et al.38 proposed an active learning procedure for Bayesian deep learning models, where new samples are added in each iteration...
But first, let’s talk about how Bayesian deep learning works and why it’s critical to creating trustworthy and reliable AI systems. What is Bayesian Deep Learning? Bayesian Deep Learning (BDL) combines the strengths of Bayesian probability theory with deep learning and enables uncer...
BO is essentially a Bayesian approach based on Bayes' theorem. The purpose of Bayesian approaches is to use the information obtained from the data as prior information and to reveal how the existing information will be updated with the obtained posterior information [36, 37]. Using the Bayesian...
BayesianUNet -> Pytorch Bayesian UNet model for segmentation and uncertainty prediction, applied to the Potsdam Dataset RAANet -> A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images wheelRuts_semanticSegmentation -> Mapping wheel-ruts from timber ...
If you use Deep Lake in your research, please cite Activeloop using: @article{deeplake, title = {Deep Lake: a Lakehouse for Deep Learning}, author = {Hambardzumyan, Sasun and Tuli, Abhinav and Ghukasyan, Levon and Rahman, Fariz and Topchyan, Hrant and Isayan, David and Harutyunyan, Mi...
Using neural networks, models were trained with small datasets, making them usable in the small data regime for image segmentation. This framework revealed that DL can reduce a significant number of biologically relevant errors (Caicedo et al. 2019). A novel DL-guided Bayesian inference (DLBI) ...
Task-Aware Variational Adversarial Active Learning (TA-VAAL)认为data distribution-based method不能很好地适应具体的任务,于是在VAAL的基础上添加了loss prediction和RankCGAN。 3.1.3 Deep Bayesian Active Learning (DBAL) DBAL将贝叶斯卷积神经网络同AL方法进行结合,使BALD适应了深度学习环境,从而为高维数据开发了一...