135,196 research outputs found
Deep Active Learning for Multi-Label Classification of Remote Sensing Images
In this letter, we introduce deep active learning (AL) for multi-label
classification (MLC) problems in remote sensing (RS). In particular, we
investigate the effectiveness of several AL query functions for MLC of RS
images. Unlike the existing AL query functions (which are defined for
single-label classification or semantic segmentation problems), each query
function in this paper is based on the evaluation of two criteria: i)
multi-label uncertainty; and ii) multi-label diversity. The multi-label
uncertainty criterion is associated to the confidence of the deep neural
networks (DNNs) in correctly assigning multi-labels to each image. To assess
this criterion, we investigate three strategies: i) learning multi-label loss
ordering; ii) measuring temporal discrepancy of multi-label predictions; and
iii) measuring magnitude of approximated gradient embeddings. The multi-label
diversity criterion is associated to the selection of a set of images that are
as diverse as possible to each other that prevents redundancy among them. To
assess this criterion, we exploit a clustering based strategy. We combine each
of the above-mentioned uncertainty strategies with the clustering based
diversity strategy, resulting in three different query functions. All the
considered query functions are introduced for the first time in the framework
of MLC problems in RS. Experimental results obtained on two benchmark archives
show that these query functions result in the selection of a highly informative
set of samples at each iteration of the AL process.Comment: Accepted to IEEE Geoscience and Remote Sensing Letter
Minimizing Supervision in Multi-label Categorization
Multiple categories of objects are present in most images. Treating this as a
multi-class classification is not justified. We treat this as a multi-label
classification problem. In this paper, we further aim to minimize the
supervision required for providing supervision in multi-label classification.
Specifically, we investigate an effective class of approaches that associate a
weak localization with each category either in terms of the bounding box or
segmentation mask. Doing so improves the accuracy of multi-label
categorization. The approach we adopt is one of active learning, i.e.,
incrementally selecting a set of samples that need supervision based on the
current model, obtaining supervision for these samples, retraining the model
with the additional set of supervised samples and proceeding again to select
the next set of samples. A crucial concern is the choice of the set of samples.
In doing so, we provide a novel insight, and no specific measure succeeds in
obtaining a consistently improved selection criterion. We, therefore, provide a
selection criterion that consistently improves the overall baseline criterion
by choosing the top k set of samples for a varied set of criteria. Using this
criterion, we are able to show that we can retain more than 98% of the fully
supervised performance with just 20% of samples (and more than 96% using 10%)
of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach
consistently outperforms all other baseline metrics for all benchmark datasets
and model combinations.Comment: Accepted in CVPR-W 202
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?
Spiking neural networks (SNNs) equipped with latency coding and spike-timing
dependent plasticity rules offer an alternative to solve the data and energy
bottlenecks of standard computer vision approaches: they can learn visual
features without supervision and can be implemented by ultra-low power hardware
architectures. However, their performance in image classification has never
been evaluated on recent image datasets. In this paper, we compare SNNs to
auto-encoders on three visual recognition datasets, and extend the use of SNNs
to color images. The analysis of the results helps us identify some bottlenecks
of SNNs: the limits of on-center/off-center coding, especially for color
images, and the ineffectiveness of current inhibition mechanisms. These issues
should be addressed to build effective SNNs for image recognition
Many Task Learning with Task Routing
Typical multi-task learning (MTL) methods rely on architectural adjustments
and a large trainable parameter set to jointly optimize over several tasks.
However, when the number of tasks increases so do the complexity of the
architectural adjustments and resource requirements. In this paper, we
introduce a method which applies a conditional feature-wise transformation over
the convolutional activations that enables a model to successfully perform a
large number of tasks. To distinguish from regular MTL, we introduce Many Task
Learning (MaTL) as a special case of MTL where more than 20 tasks are performed
by a single model. Our method dubbed Task Routing (TR) is encapsulated in a
layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario
successfully fits hundreds of classification tasks in one model. We evaluate
our method on 5 datasets against strong baselines and state-of-the-art
approaches.Comment: 8 Pages, 5 Figures, 2 Table
Cancer diagnosis using deep learning: A bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements
- …