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Title: LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
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Comments: ECCV'18 (European Conference on Computer Vision); Main paper + suppl. material
Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI)

Although weight and activation quantization is an effective approach for Deep Neural Network (DNN) compression and has a lot of potentials to increase inference speed leveraging bit-operations, there is still a noticeable gap in terms of prediction accuracy between the quantized model and the full-precision model. To address this gap, we propose to jointly train a quantized, bit-operation-compatible DNN and its associated quantizers, as opposed to using fixed, handcrafted quantization schemes such as uniform or logarithmic quantization. Our method for learning the quantizers applies to both network weights and activations with arbitrary-bit precision, and our quantizers are easy to train. The comprehensive experiments on CIFAR-10 and ImageNet datasets show that our method works consistently well for various network structures such as AlexNet, VGG-Net, GoogLeNet, ResNet, and DenseNet, surpassing previous quantization methods in terms of accuracy by an appreciable margin. Code available at https://github.com/Microsoft/LQ-Nets

Title: Motion Feature Network: Fixed Motion Filter for Action Recognition
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Comments: ECCV 2018, 14 pages, 6 figures, 4 tables
Subjects: Computer Vision and Pattern Recognition (cs.CV)

Spatio-temporal representations in frame sequences play an important role in the task of action recognition. Previously, a method of using optical flow as a temporal information in combination with a set of RGB images that contain spatial information has shown great performance enhancement in the action recognition tasks. However, it has an expensive computational cost and requires two-stream (RGB and optical flow) framework. In this paper, we propose MFNet (Motion Feature Network) containing motion blocks which make it possible to encode spatio-temporal information between adjacent frames in a unified network that can be trained end-to-end. The motion block can be attached to any existing CNN-based action recognition frameworks with only a small additional cost. We evaluated our network on two of the action recognition datasets (Jester and Something-Something) and achieved competitive performances for both datasets by training the networks from scratch.

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iPhone users have been running out of storage space ever since the first iPhone was released more than a decade ago. In 2017, there are two main culprits. The lesser issue is typically the Messages app, since many people are unaware that all those photos and videos they receive are stored locally on their phones. That often adds up to gigabytes upon gigabytes of storage that gets gobbled up by media that will never be viewed again.

The bigger issue is all the storage that’s eaten up by photos and videos captured by the iPhone itself.

As smartphone camera quality continues to improve with each new product generation, photo and video sizes get bigger and bigger. Apple’s new high-efficiency photo format was developed to help the issue a bit, but it makes sharing photos with anyone who doesn’t also have an iPhone a huge pain. (Don’t worry, you can disable it by going to Settings > Camera > Formats and selecting “Most Compatible.”)

But Apple’s HEIC photo format only does so much. People still often take hundreds of new photos with their iPhones each week, and 4K video files can be massive. Even if you pay an extra $150 to bump up your iPhone to 256GB of storage from 64GB, all those gigabytes still tend to disappear quickly. That’s where Google Photos comes in.

Apple’s iCloud service is nifty, but it’s not free and it’s actually not a very good solution to this problem. iCloud is designed to synchronize photos and videos across devices by default, which means these massive media files now occupy space on several devices. Google Photos is different. With this smart solution, you can upload all your photos and videos to Google servers and then delete them off your phone. You’ll still see all the thumbnails in your Google Photos app and you can browse through them anytime you want, then tap on a photo or video and it will quickly be downloaded and displayed. You can also take advantage of Google’s AI features like automatic smart album sorting, facial recognition, object recognition, and more. Apple has adopted some of these features in its own photos app, but Google’s AI continues to be vastly superior to Apple’s.

To this day, I continue to see friends and family get the dreaded “Cannot Take Photo” error because they’re out of storage, or the “Not Enough Space” error when they try to update iOS. Emails also come in all the time from people complaining about how quickly they run out of storage. Rochas pleated ruffle trim Tshirt Outlet Finishline z943ll
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However, this move strikes me as completely ad hoc — it is ad hoc to diagnose our (expected) reaction to the uncle-Smith-type scenario in this way, but not allow the corresponding move to an advocate of the target proposal. Is it not just as plausible (or implausible) that our reaction to the more distant realizations that Williamson exploits against that proposal is erroneous?

Note that I am not just arguing that, for all we know , Williamson’s content proposal is mistaken since, for all we know, the actual or a nearby world contains a deviant realization of the Gettier case (and so, for all we know, counterfactual is false). The point is that, on Williamson’s view, there could be no deviant but actual or nearby non-actual realizations. But that seems wrong: on the face of it, the uncle Smith realization is one such realization, and it is easy to come up with more. This, I submit, is already enough to refute the proposal. In his response, Williamson does not contest the possibility of uncle-Smith-type scenarios, only their deviance. But he does not explain away their apparent deviance. And it is very hard to see how to do that in a way that does not backfire. To reiterate: why think that we are poor judges of deviance when it comes to nearby realizations of the case, but reliable when it comes to more distant realizations? (If indeed there is an asymmetry here, one might expect it to be the other way round.)

The above consideration seems to suggest that counterfactual is too strong; that the range of possible realizations it permits is too wide. In light of this, one might replace counterfactual with its mere possibility — the claim that it is possible that, if someone were to stand to a proposition as in the case as described, then she would have a justified true belief without knowledge. 31 The embedded counterfactual would not be falsified by the uncle Smith realization; moreover, it is something that we plausibly believe, that we have justification to believe, and that (arguably) would yield a good inference.

But this proposal seems like overkill, given that there is another, simpler possibility claim in the vicinity — one that it is also plausible that we believe, that we have justification to believe, that would not be falsified by the uncle Smith realization, and that yields a good inference, namely: I suggest that this is the real content of our judgement. If that is right, it looks like the Gettier inference has a very simple structure: other things equal, we can rationally infer that the JTB theory is false directly from the Gettier judgement. 33

possibility  It is possible that someone stands to p as in the Gettier case (as described) and that she has a justified true belief that p but does not know that p . 32

I will now defend this suggestion against some challenges and rival views.

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