Christian Szegedy, Wei Liu, Yangqing Jia
Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
2014 · arXiv
We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
Metadata: arXiv. Abstracts retain their original authors’ rights.
Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels
4 selected excerpts · 4 total mentions
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
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 the difference between our denotative language and our experience of language!…
https://www.auduno.com/2015/07/29/visualizing-googlenet-clas... Also, for those curious about the DepthConcat operation, it's described here: https://stats.stackexchange.com/questions/184823/how-does-th... Edit: I'll be damned if there isn't something downright Jungian about these prototypes! There are snakes! Man-made objects! Shelter structures! Wheels! Animals! Sexy legs! The connection between snakes and guitar bodies is blowing my mind!
[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 computation that will need to performed to train the network to some level. In fact, in a…
recent paper [2] some folks at Google describe how they achieved great performance training a huge CNN by inserting temporary output layers in the middle of the network while training (among other things). This increased the amount of gradient information that was propagated back to the bottom layers, forcing them to learn more powerful representations. [1] https://en.wikipedia.org/wiki/Backpropagation [2] http://arxiv.org/abs/1409.4842
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
More from the same topics.
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan +11
Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition.
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task.
Chiyuan Zhang, Samy Bengio, Moritz Hardt +2
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance.
A. Pluchino. A. E. Biondo, A. Rapisarda
The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is due mainly, if not exclusively, to personal qualities such as talent, intelligence, skills, efforts or risk taking.