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Selection: with tag deep-learning [9 articles] 

 

Deep Learning

  
(2016)
edited by M. I. T. Press

Abstract

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. ...

 

Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks

  
In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016) (2016), pp. 2874-2883, https://doi.org/10.1109/CVPR.2016.314

Abstract

It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. Through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are ...

 

Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling

  
(2015)

Abstract

We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding ...

 

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

  
(10 Oct 2016)

Abstract

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the ...

 

Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification

  
Pattern Recognition, Vol. 61 (4 Feb 2016), pp. 539-556, https://doi.org/10.1016/j.patcog.2016.07.001

Abstract

We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors. In many applications, especially including remote sensing, it is not feasible to fully design and train a new ConvNet, as this usually requires a considerable amount of labeled data and demands high computational costs. Therefore, it is important to understand how to obtain the best profit from existing ConvNets. We perform ...

 

Deep learning in remote sensing: a review

  
IEEE Geoscience and Remote Sensing Magazine, Vol. 5, No. 4. (11 Oct 2017), pp. 8-36, https://doi.org/10.1109/mgrs.2017.2762307

Abstract

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep ...

 

Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images

  
Remote Sensing, Vol. 9, No. 4. (13 April 2017), 368, https://doi.org/10.3390/rs9040368

Abstract

Like computer vision before, remote sensing has been radically changed by the introduction of deep learning and, more notably, Convolution Neural Networks. Land cover classification, object detection and scene understanding in aerial images rely more and more on deep networks to achieve new state-of-the-art results. Recent architectures such as Fully Convolutional Networks can even produce pixel level annotations for semantic mapping. In this work, we present a deep-learning based segment-before-detect method for segmentation and subsequent detection and classification of several varieties ...

 

Speed/accuracy trade-offs for modern convolutional object detectors

  
In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) (2017), pp. 7310-7319, https://doi.org/10.1109/CVPR.2017.351

Abstract

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-to-apples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 11

  
(February 2014)
Keywords: dasineura-salicis   data   data-acquisition   data-based-mechanistic-modelling   data-breach   data-collection-bias   data-errors   data-fusion   data-heterogeneity   data-integration   data-lineage   data-model-comparison   data-provenance   data-quality   data-scarcity   data-sharing   data-transformation-codelets   data-transformation-modelling   data-transformation-modelling-dynamic   data-uncertainty   database   dataset   dating   davidsoniella-virescens   dbh   dddas   de-facto-standard   dead-wood   debris   debris-floods   debris-flows   deciduous   deciduous-forest   decision-making   decision-making-procedure   decision-support-system   decline   decline-effect   decline-symptomology   deep-learning   deep-machine-learning   deep-reproducible-research   deep-uncertainty   defensible-space   definition   defoliation   deforestation   degenerated-soil   deglaciation   degradation   degradation-velocity   dehesas   delay   delonix-regia   democracy   demographic-indicators   dendrochronology   dendroctonus-frontalis   dendroctonus-micans   dendroctonus-ponderosae   dendroctonus-pseudotsugae   dendroctonus-rufipennis   dendroctonus-spp   dendroecology   dendrology   denmark   density-related-behaviour   deposition   derived-data   desalinisation   description   desertification   deserts   design-diversity   development   devil-in-details   diabetes   diabetes-mellitus   diagram-data   diameter-differentiation   dictionary   dictyophara-europea   die-off   dieback   diesel   difference   differentiation   digital-preservation   digital-society   dimensional-analysis   dimensionality-reduction   dimensionless   dioryctria-splendidella   dioscorea-caucasica   diospyros-kaki   diospyros-lotus   diospyros-spp   diospyros-virginiana   diplodia-pinea   inrmm-list-of-tags  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/deep-learning

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.