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Cost Sensitive Learning of Deep Feature Representations from Imbalanced
  Data

Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data

14 August 2015
Salman H. Khan
Munawar Hayat
Bennamoun
Ferdous Sohel
R. Togneri
ArXivPDFHTML

Papers citing "Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data"

20 / 20 papers shown
Title
Fractal Calibration for long-tailed object detection
Fractal Calibration for long-tailed object detection
Konstantinos Panagiotis Alexandridis
Ismail Elezi
Jiankang Deng
Anh H. Nguyen
Shan Luo
380
0
0
15 Oct 2024
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Yuzhe Yang
Zhi Xu
SSL
122
407
0
13 Jun 2020
Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning
Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning
Yu-An Chung
Hsuan-Tien Lin
Shao-Wen Yang
50
82
0
30 Nov 2015
Batch-normalized Maxout Network in Network
Batch-normalized Maxout Network in Network
Jia-Ren Chang
Yonghao Chen
OOD
88
108
0
09 Nov 2015
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed,
  Gated, and Tree
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
Chen-Yu Lee
Patrick W. Gallagher
Zhuowen Tu
AI4CE
73
484
0
30 Sep 2015
A Discriminative Representation of Convolutional Features for Indoor
  Scene Recognition
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
Salman H. Khan
Munawar Hayat
Bennamoun
R. Togneri
Ferdous Sohel
61
71
0
17 Jun 2015
Deeply-Supervised Nets
Deeply-Supervised Nets
Chen-Yu Lee
Saining Xie
Patrick W. Gallagher
Zhengyou Zhang
Zhuowen Tu
327
2,238
0
18 Sep 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAtt
MDE
1.4K
100,213
0
04 Sep 2014
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
  Recognition
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
ObjD
357
11,199
0
18 Jun 2014
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Ken Chatfield
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
204
3,416
0
14 May 2014
CNN Features off-the-shelf: an Astounding Baseline for Recognition
CNN Features off-the-shelf: an Astounding Baseline for Recognition
A. Razavian
Hossein Azizpour
Josephine Sullivan
S. Carlsson
147
4,938
0
23 Mar 2014
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Multi-scale Orderless Pooling of Deep Convolutional Activation Features
Yunchao Gong
Liwei Wang
Ruiqi Guo
Svetlana Lazebnik
176
1,090
0
07 Mar 2014
Improving Deep Neural Networks with Probabilistic Maxout Units
Improving Deep Neural Networks with Probabilistic Maxout Units
Jost Tobias Springenberg
Martin Riedmiller
BDL
OOD
196
101
0
20 Dec 2013
Network In Network
Network In Network
Min Lin
Qiang Chen
Shuicheng Yan
279
6,274
0
16 Dec 2013
Visualizing and Understanding Convolutional Networks
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler
Rob Fergus
FAtt
SSL
519
15,861
0
12 Nov 2013
Rich feature hierarchies for accurate object detection and semantic
  segmentation
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick
Jeff Donahue
Trevor Darrell
Jitendra Malik
ObjD
270
26,168
0
11 Nov 2013
DeCAF: A Deep Convolutional Activation Feature for Generic Visual
  Recognition
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue
Yangqing Jia
Oriol Vinyals
Judy Hoffman
Ning Zhang
Eric Tzeng
Trevor Darrell
VLM
ObjD
176
4,948
0
06 Oct 2013
Maxout Networks
Maxout Networks
Ian Goodfellow
David Warde-Farley
M. Berk Mirza
Aaron Courville
Yoshua Bengio
OOD
224
2,177
0
18 Feb 2013
Stochastic Pooling for Regularization of Deep Convolutional Neural
  Networks
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler
Rob Fergus
172
989
0
16 Jan 2013
SMOTE: Synthetic Minority Over-sampling Technique
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh Chawla
Kevin W. Bowyer
Lawrence Hall
W. Kegelmeyer
AI4TS
337
25,569
0
09 Jun 2011
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