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PsyPhy: A Psychophysics Driven Evaluation Framework for Visual
  Recognition

PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition

19 November 2016
Brandon RichardWebster
Samuel E. Anthony
Walter J. Scheirer
ArXivPDFHTML

Papers citing "PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition"

16 / 16 papers shown
Title
Comparing deep neural networks against humans: object recognition when
  the signal gets weaker
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Robert Geirhos
David H. J. Janssen
Heiko H. Schutt
Jonas Rauber
Matthias Bethge
Felix Wichmann
72
244
0
21 Jun 2017
Learning a Probabilistic Latent Space of Object Shapes via 3D
  Generative-Adversarial Modeling
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Jiajun Wu
Chengkai Zhang
Tianfan Xue
Bill Freeman
J. Tenenbaum
GAN
308
1,953
0
24 Oct 2016
How Deep is the Feature Analysis underlying Rapid Visual Categorization?
How Deep is the Feature Analysis underlying Rapid Visual Categorization?
S. Eberhardt
Jonah Cader
Thomas Serre
61
53
0
03 Jun 2016
Can we still avoid automatic face detection?
Can we still avoid automatic face detection?
Michael J. Wilber
Vitaly Shmatikov
Serge J. Belongie
PICV
CVBM
59
56
0
14 Feb 2016
Towards Open Set Deep Networks
Towards Open Set Deep Networks
Abhijit Bendale
Terrance Boult
BDL
EDL
104
1,426
0
19 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
821
9,306
0
06 Jun 2015
A Deeper Look at Dataset Bias
A Deeper Look at Dataset Bias
Tatiana Tommasi
Novi Patricia
Barbara Caputo
Tinne Tuytelaars
97
327
0
06 May 2015
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
161
3,271
0
05 Dec 2014
Going Deeper with Convolutions
Going Deeper with Convolutions
Christian Szegedy
Wei Liu
Yangqing Jia
P. Sermanet
Scott E. Reed
Dragomir Anguelov
D. Erhan
Vincent Vanhoucke
Andrew Rabinovich
462
43,649
0
17 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
1.6K
100,348
0
04 Sep 2014
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
1.7K
39,525
0
01 Sep 2014
scikit-image: Image processing in Python
scikit-image: Image processing in Python
Stéfan van der Walt
Johannes L. Schonberger
Juan Nunez-Iglesias
François Boulogne
Joshua D. Warner
Neil Yager
Emmanuelle Gouillart
Tony Yu
the scikit-image contributors
SSeg
GP
VLM
188
4,351
0
23 Jul 2014
Caffe: Convolutional Architecture for Fast Feature Embedding
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia
Evan Shelhamer
Jeff Donahue
Sergey Karayev
Jonathan Long
Ross B. Girshick
S. Guadarrama
Trevor Darrell
VLM
BDL
3DV
274
14,710
0
20 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
215
3,418
0
14 May 2014
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
270
14,918
1
21 Dec 2013
The Neural Representation Benchmark and its Evaluation on Brain and
  Machine
The Neural Representation Benchmark and its Evaluation on Brain and Machine
C. Cadieu
Ha Hong
Daniel L. K. Yamins
Nicolas Pinto
N. Majaj
J. DiCarlo
OOD
SSL
79
32
0
15 Jan 2013
1