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arXiv:1409.4842cs.CV

Going Deeper with Convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

Abstract

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.

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DOI
Subjects
cs.CVComputer Vision and Pattern Recognition

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Linked by 4 identifiable commenters in 4 comments across 4 threads, from Mar 2015 to Apr 2022. Found in 1 HN submission from Sep 2014 to Sep 2014. Open submission discussions.

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Discussed in comments4 threads

  1. DeepMind’s New Language Model, Chinchilla 1 comment

    • 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…

      jstx1View on HNParent context

  2. Feature Visualization: How neural nets build up their understanding of images 1 comment

    • 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…

      chillingeffectView on HNParent context

  3. 10B Parameter Neural Networks in Your Basement [pdf] 1 comment

    • [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…

      andbbergerView on HNParent context

  4. Show HN: Anime character recognizer 1 comment

    • 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…

      avalletView on HNParent context

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