Going Deeper with Convolutions · HackerPapers discussions

Going Deeper with Convolutions — mentioned by jstx1

Mon, 11 Apr 2022 15:00:40 GMT

Image models too - the Inception paper from 2014 directly refers to knowyourmeme.com and the "we need to go deeper" meme from the movie Inception - https://knowyourmeme.com/memes/we-need-to-go-deeper - it's the first reference in the paper [1] and it's also why the model is called that way. [1] https://arxiv.org/pdf/1409.4842.pdf

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Going Deeper with Convolutions — mentioned by chillingeffect

Tue, 07 Nov 2017 22:04:07 GMT

Are the layer names the same ones referred to in this paper? https://arxiv.org/abs/1409.4842 And how can e.g. layer3a be generated from layer conv2d0? By convolving with a linear kernel? Or by the entire Inception Module including the linear and the non-linear operations? Thank you. Outstanding work breaking it down. Here's another paper people might enjoy. The author generates an example for "Saxophone," which includes a player... Which is fascinating, bc it implies that our usage of the word in real practice implies a player, even though the Saxophone is an instrument only. This highlights…

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Going Deeper with Convolutions — mentioned by andbberger

Thu, 02 Jul 2015 01:00:10 GMT

[citation needed] Sure, there's a huge amount of research happening in the field right now. But you make it sound like there's a ton of low hanging fruit, which is emphatically not true. Also, freezing the representations in the bottom layers usually doesn't lead to very good results. The representation the bottom layers learn in the standard formulation of deep feedforward networks is informed by gradient information in the output layers[1]. If you stop training the bottom layers after some time you're sacrificing representational power and in fact increasing the ultimate amount of…

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Going Deeper with Convolutions — mentioned by avallet

Sun, 08 Mar 2015 04:25:08 GMT

Hi! Author here. The blog post and source code refer to an older algorithm than the one in the demo. Currently it is using a convolutional network for prediction not dissimilar to [1] or [2]. I'm writing a paper about it, if there is interest I'll put it online :) . Thank you for the feedback on incorrectly identified characters! The algorithm is not quite perfect - 70% accuracy on a randomly chosen test set. [1]: http://arxiv.org/abs/1409.4842 [2]: http://arxiv.org/abs/1409.1556

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Going Deeper with Convolutions — HN submission

Fri, 19 Sep 2014 15:01:57 GMT

Going Deeper with Convolutions (GoogLeNet Paper)

4 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.