scholarly journals PARTNER: Human-in-the-Loop Entity Name Understanding with Deep Learning

2020 ◽  
Vol 34 (09) ◽  
pp. 13634-13635
Author(s):  
Kun Qian ◽  
Poornima Chozhiyath Raman ◽  
Yunyao Li ◽  
Lucian Popa
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Important Task ◽  
Interactive System ◽  
Name Disambiguation ◽  
Weak Supervision ◽  
Human In The Loop ◽  
Human Effort

Entity name disambiguation is an important task for many text-based AI tasks. Entity names usually have internal semantic structures that are useful for resolving different variations of the same entity. We present, PARTNER, a deep learning-based interactive system for entity name understanding. Powered by effective active learning and weak supervision, PARTNER can learn deep learning-based models for identifying entity name structure with low human effort. PARTNER also allows the user to design complex normalization and variant generation functions without coding skills.

scholarly journals Interactive Deep Learning for Shelf Life Prediction of Muskmelons Based on an Active Learning Approach

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 414
Author(s):  
Dominique Albert-Weiss ◽  
Ahmad Osman
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Shelf Life ◽  
Point Processes ◽  
Feature Space ◽  
Determinantal Point Processes ◽  
Rejection Sampling ◽  
Efficient Manner ◽  
Human In The Loop ◽  
High Data

A pivotal topic in agriculture and food monitoring is the assessment of the quality and ripeness of agricultural products by using non-destructive testing techniques. Acoustic testing offers a rapid in situ analysis of the state of the agricultural good, obtaining global information of its interior. While deep learning (DL) methods have outperformed state-of-the-art benchmarks in various applications, the reason for lacking adaptation of DL algorithms such as convolutional neural networks (CNNs) can be traced back to its high data inefficiency and the absence of annotated data. Active learning is a framework that has been heavily used in machine learning when the labelled instances are scarce or cumbersome to obtain. This is specifically of interest when the DL algorithm is highly uncertain about the label of an instance. By allowing the human-in-the-loop for guidance, a continuous improvement of the DL algorithm based on a sample efficient manner can be obtained. This paper seeks to study the applicability of active learning when grading ‘Galia’ muskmelons based on its shelf life. We propose k-Determinantal Point Processes (k-DPP), which is a purely diversity-based method that allows to take influence on the exploration within the feature space based on the chosen subset k. While getting coequal results to uncertainty-based approaches when k is large, we simultaneously obtain a better exploration of the data distribution. While the implementation based on eigendecomposition takes up a runtime of O(n3), this can further be reduced to O(n·poly(k)) based on rejection sampling. We suggest the use of diversity-based acquisition when only a few labelled samples are available, allowing for better exploration while counteracting the disadvantage of missing the training objective in uncertainty-based methods following a greedy fashion.

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation

2021 ◽  
Vol 136 ◽  
pp. 95-102
Author(s):  
Marika Cusick ◽  
Prakash Adekkanattu ◽  
Thomas R. Campion ◽  
Evan T. Sholle ◽  
Annie Myers ◽  
...  
Keyword(s):  
Deep Learning ◽  
Suicidal Ideation ◽  
Weak Supervision ◽  
Clinical Notes

scholarly journals Biomedical Image Segmentation via Representative Annotation

2019 ◽  
Vol 33 ◽  
pp. 5901-5908 ◽  
Author(s):  
Hao Zheng ◽  
Lin Yang ◽  
Jianxu Chen ◽  
Jun Han ◽  
Yizhe Zhang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Active Learning ◽  
Iterative Process ◽  
Image Data ◽  
Manual Annotation ◽  
Convolutional Network ◽  
Biomedical Image ◽  
Image Patches ◽  
Representative Image

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

Introducing AIDE: a Software Suite for Annotating Images with Deep and Active Learning Assistance

2021 ◽  
Author(s):  
Benjamin Kellenberger ◽  
Devis Tuia ◽  
Dan Morris
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Expert Knowledge ◽  
Semantic Segmentation ◽  
Third Party ◽  
Learning Models ◽  
Web Browser ◽  
Web Based ◽  
Model Training ◽  
Bounding Boxes

<p>Ecological research like wildlife censuses increasingly relies on data on the scale of Terabytes. For example, modern camera trap datasets contain millions of images that require prohibitive amounts of manual labour to be annotated with species, bounding boxes, and the like. Machine learning, especially deep learning [3], could greatly accelerate this task through automated predictions, but involves expansive coding and expert knowledge.</p><p>In this abstract we present AIDE, the Annotation Interface for Data-driven Ecology [2]. In a first instance, AIDE is a web-based annotation suite for image labelling with support for concurrent access and scalability, up to the cloud. In a second instance, it tightly integrates deep learning models into the annotation process through active learning [7], where models learn from user-provided labels and in turn select the most relevant images for review from the large pool of unlabelled ones (Fig. 1). The result is a system where users only need to label what is required, which saves time and decreases errors due to fatigue.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.0402be60f60062057601161/sdaolpUECMynit/12UGE&app=m&a=0&c=131251398e575ac9974634bd0861fadc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: AIDE offers concurrent web image labelling support and uses annotations and deep learning models in an active learning loop.</em></p><p>AIDE includes a comprehensive set of built-in models, such as ResNet [1] for image classification, Faster R-CNN [5] and RetinaNet [4] for object detection, and U-Net [6] for semantic segmentation. All models can be customised and used without having to write a single line of code. Furthermore, AIDE accepts any third-party model with minimal implementation requirements. To complete the package, AIDE offers both user annotation and model prediction evaluation, access control, customisable model training, and more, all through the web browser.</p><p>AIDE is fully open source and available under https://github.com/microsoft/aerial_wildlife_detection.</p><p> </p><p><strong>References</strong></p>

scholarly journals An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 901
Author(s):  
Fucong Liu ◽  
Tongzhou Zhang ◽  
Caixia Zheng ◽  
Yuanyuan Cheng ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Active Learning ◽  
Sample Selection ◽  
Training Dataset ◽  
Learning Method ◽  
Active Learning Method ◽  
Level Information ◽  
The Stability ◽  
Different Characteristics

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.

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