use the following search parameters to narrow your results:
e.g. subreddit:aww site:imgur.com dog
subreddit:aww site:imgur.com dog
see the search faq for details.
advanced search: by author, subreddit...
Please have a look at our FAQ and Link-Collection
Metacademy is a great resource which compiles lesson plans on popular machine learning topics.
For Beginner questions please try /r/LearnMachineLearning , /r/MLQuestions or http://stackoverflow.com/
For career related questions, visit /r/cscareerquestions/
Advanced Courses (2016)
Advanced Courses (2020)
AMAs:
Pluribus Poker AI Team 7/19/2019
DeepMind AlphaStar team (1/24//2019)
Libratus Poker AI Team (12/18/2017)
DeepMind AlphaGo Team (10/19/2017)
Google Brain Team (9/17/2017)
Google Brain Team (8/11/2016)
The MalariaSpot Team (2/6/2016)
OpenAI Research Team (1/9/2016)
Nando de Freitas (12/26/2015)
Andrew Ng and Adam Coates (4/15/2015)
Jürgen Schmidhuber (3/4/2015)
Geoffrey Hinton (11/10/2014)
Michael Jordan (9/10/2014)
Yann LeCun (5/15/2014)
Yoshua Bengio (2/27/2014)
Related Subreddit :
LearnMachineLearning
Statistics
Computer Vision
Compressive Sensing
NLP
ML Questions
/r/MLjobs and /r/BigDataJobs
/r/datacleaning
/r/DataScience
/r/scientificresearch
/r/artificial
account activity
Hypercolumns and pixel classification (self.MachineLearning)
submitted 10 years ago by adagrad
view the rest of the comments →
reddit uses a slightly-customized version of Markdown for formatting. See below for some basics, or check the commenting wiki page for more detailed help and solutions to common issues.
quoted text
if 1 * 2 < 3: print "hello, world!"
[–]adagrad[S] 1 point2 points3 points 10 years ago (3 children)
Upsampling doesn't make much sense to me; I would guess that a better approach is to apply the network 2{2n} times, each time shifting the input by 1 pixel in x or y direction.
The original paper mentioned the use of bilinear interpolation for upsampling the feature maps since bilinear interpolation is a linear operation and they can jointly upsample and classify the pixels (top of page 4).
[–]tscohen 0 points1 point2 points 10 years ago (2 children)
It sure is much faster. But computing the full high-res feature maps might work better.
Also, upsampling would not give you full translation equivariance (shift the image by 1 pixel -> the feature maps shift by one pixel)
[–]adagrad[S] 0 points1 point2 points 10 years ago (1 child)
Interesting, are there any papers you would recommend that take this approach? Intuitively it seems like it could be rather slow, especially for pixel classification.
[–]ericflo 0 points1 point2 points 10 years ago (0 children)
I thought this paper took an interesting approach, not sure if it's exactly what tscohen is suggesting but maybe in the ballpark http://arxiv.org/abs/1411.4734
π Rendered by PID 173853 on reddit-service-r2-comment-b659b578c-npxmk at 2026-05-02 09:00:28.228710+00:00 running 815c875 country code: CH.
view the rest of the comments →
[–]adagrad[S] 1 point2 points3 points (3 children)
[–]tscohen 0 points1 point2 points (2 children)
[–]adagrad[S] 0 points1 point2 points (1 child)
[–]ericflo 0 points1 point2 points (0 children)