ECCV 2018 had BodyNet which was SOTA. What is CVPR 2019 state of art?

I am trying to implement a GLOW model for a project work. While working on it I came across this implementation https://github.com/chrischute/glow. I don’t understand how does the calculation of prior_ll work here in the loss function.

It would be great if anyone could help me understand it!

The more I get into DL, the more I’m convinced our brains are just a DL model created by some Alien race on (god forbid) Caffe.

I recently came across an article about Saccadic masking, and it got me thinking. Actually it just falls into place with this theory I’ve had for a while, and it’s quite simple: We’re running a very advanced version of some DL model (hopefully Resnet50).

I know we’ve moved away from the brain and neural network comparisons in recent years, but I think it’s time we bring them back.

I am an undergraduate and have been doing ML/NLP/DL/RL for past two years and was interested in applying for master’s. I wish to pursue PhD (in NLP/CV) at some point, so was hoping to join thesis based research programmes. I was hoping if someone could suggest good programmes (relatively less competetive). I have spent an entire week on csrankings.org and have shortlisted few colleges. Please don’t suggest obvious ones (GaTech, berkeley etc.). I want to work under a really good advisor (more publications and citations) and where the chances of me working them is more and environment is purely research focussed (like MILA for ex.). I am looking for these programmes in US/Canada. If anyone has some prior experience with applications or a good idea, please give suggestions. I know it’s subjective, but still a general idea would be helpful.

What do matrix multiplication, ReLU, and max pooling all have in common? Yes their second derivatives are all zero, but there is another interesting property that they all satisfy:

f(a x) = a f(x) 

Which means that, when you stack these on top of each other, scaling the input of the network by some constant is equivalent to scaling the output by some constant. Moreover, there are cases where the scale of the output doesn’t matter (e.g. if predicted classes are based on argmax of the network output).

This leaves us in an interesting situation where it’s actually very easy to encode complete brightness invariance in a network — where you can always brighten/darken an image by some factor without affecting its predictions (assuming you normalize the scale of the output in some way — softmax, sphere projection, etc.).

I’ve trained models with and without biases on CIFAR and find they’re both reasonable. I suspect a more rigorous comparision would find that networks without bias terms tend to do marginally worse than models with them — if only because they have fewer parameters (e.g. without bias terms you can’t learn that airplane images are usually brighter than frog images).

But in the interest of developing networks that actually generalize well to the real world (not just the random sample of your data you held out as a test set) it seems like this a modest performance gap might be permissible, if it means you can be confident that your network will work well in significantly different lighting conditions.

MIT neuroscientists have performed the most rigorous testing yet of computational models that mimic the brain’s visual cortex.

Using their current best model of the brain’s visual neural network, the researchers designed a new way to precisely control individual neurons and populations of neurons in the middle of that network. In an animal study, the team then showed that the information gained from the computational model enabled them to create images that strongly activated specific brain neurons of their choosing.

The findings suggest that the current versions of these models are similar enough to the brain that they could be used to control brain states in animals. The study also helps to establish the usefulness of these vision models, which have generated vigorous debate over whether they accurately mimic how the visual cortex works, says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, an investigator in the McGovern Institute for Brain Research and the Center for Brains, Minds, and Machines, and the senior author of the study.

​

Full article: http://news.mit.edu/2019/computer-model-brain-visual-cortex-0502

Science paper: https://science.sciencemag.org/content/364/6439/eaav9436

Biorxiv (open access): https://www.biorxiv.org/content/10.1101/461525v1