Visualizing and Understanding Convolutional Networks · HackerPapers discussions

Visualizing and Understanding Convolutional Networks — mentioned by peter_d_sherman

Wed, 07 Oct 2026 12:32:29 GMT

Related: The Building Blocks of Interpretability https://distill.pub/2018/building-blocks/ Activation Atlas https://distill.pub/2019/activation-atlas/ Visualizing and Understanding Convolutional Networks https://arxiv.org/pdf/1311.2901

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Visualizing and Understanding Convolutional Networks — mentioned by garyiskidding

Tue, 19 Mar 2024 07:47:22 GMT

The 2013 Zeiler and Fergus paper also explains this based on activations during training of the network resulting in feature detection across layers. Paper : https://arxiv.org/pdf/1311.2901.pdf

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Visualizing and Understanding Convolutional Networks — mentioned by karxxm

Sun, 08 Oct 2023 10:53:21 GMT

Some architectures are relatively well understood. Eg in CNNs, the first layers detect low level features like edges, gradients, etc. The next layer then combines these features to more complex structures like corners or circles. Next layer will combine these features to even higher level features and so on. [1] Typically, you can take a pre-trained model and retrain it on your new dataset by only changing the weights of the last layer(s). Some loss functions even measures the difference between the high-level features of two images, typically extracted from a pre-trained CNN (Perceptual…

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Visualizing and Understanding Convolutional Networks — mentioned by blueboo

Thu, 07 Sep 2023 14:37:23 GMT

There’s no helping the incurious, but there are indeed reams of explorations unto weights and their explanations. One of my personal favourites is Zeiler and Fergus https://arxiv.org/abs/1311.2901 Tom Zahavy’s “Greying the black box” on deep RL comes to mind too https://arxiv.org/abs/1602.02658 OpenAI has their Microscope tool for investigating individual neurons https://openai.com/research/microscope; more recently OpenAI shared research and tooling leveraging a powerful language model to explain a smaller model’s neurons. https://openai.com/research/language-models-can-explain-neur... For a…

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Visualizing and Understanding Convolutional Networks — HN submission

Tue, 11 Aug 2020 07:38:56 GMT

Visualizing and Understanding Convolutional Networks (2013)

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

Visualizing and Understanding Convolutional Networks — HN submission

Sat, 08 Aug 2020 22:48:27 GMT

Visualizing and Understanding Convolutional Networks (2013)

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

Visualizing and Understanding Convolutional Networks — mentioned by mcintyre1994

Sun, 18 Feb 2018 18:24:52 GMT

There's a pretty cool paper that visualises the different layers in a ConvNet trained on ImageNet: https://arxiv.org/pdf/1311.2901.pdf - page 4 has the layer imagery.

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Visualizing and Understanding Convolutional Networks — mentioned by snippyhollow

Wed, 08 Nov 2017 14:05:00 GMT

Similar to https://arxiv.org/abs/1311.2901

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Visualizing and Understanding Convolutional Networks — mentioned by hiddencost

Sun, 05 Feb 2017 00:19:44 GMT

https://arxiv.org/abs/1311.2901 Check out figure 2. The network learns composition from fundamental shapes and gradients to compositional ones. It's kinda awe inspiring. Not NN specific, but some more work on explanations: https://homes.cs.washington.edu/~marcotcr/blog/lime/ Visualization of attention mechanisms is pretty cool for explanations: https://arxiv.org/pdf/1502.03044.pdf http://torch.ch/blog/2015/09/21/rmva.html

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Visualizing and Understanding Convolutional Networks — mentioned by augustt

Fri, 19 Aug 2016 08:35:09 GMT

It's definitely possible to get insight into how a CNN would classify something like a stop sign. This paper does a good job of showing how CNNs learn a hierarchy of increasingly complex features to classify images: http://arxiv.org/abs/1311.2901

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Visualizing and Understanding Convolutional Networks — mentioned by sxyuan

Thu, 30 Jul 2015 03:20:25 GMT

Seeing the face of a dog just means that the network has learned a good representation of dog faces. I don't think that has anything to do with the generalization ability of the network. I'm not clear on the details of this hallucination process, but this reminds me of earlier work by Zeiler & Fergus[1], where you can see the bottom layers of the network learning general low-level features and later layers learning higher level representations of the objects being classified. [1] http://arxiv.org/abs/1311.2901

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Visualizing and Understanding Convolutional Networks — mentioned by choppaface

Thu, 29 Jan 2015 22:34:16 GMT

A few other useful resources to play with convolutional kernels: * The linked demo really focuses only on layer 1. See Layers >=2 plotted here: http://arxiv.org/pdf/1311.2901v3.pdf * DeepViz is a nice tool: https://github.com/bruckner/deepViz * Another tool one can use to play with convolutional kernels is ShaderToy ( https://www.shadertoy.com/ ). Here's a Gaussian blur: https://www.shadertoy.com/view/XdfGDH * If you like playing with kernels in shaders, see also Brad Larson's GPUImage: ( https://github.com/BradLarson/GPUImage ) -- the demo app has a bunch of standard kernels. Just for fun…

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Visualizing and Understanding Convolutional Networks — mentioned by colah3

Thu, 22 Jan 2015 18:16:45 GMT

You make some pretty strong attacks on the state of the art approach to computer vision. Could you please provide citations? As someone working on neural networks, although not really on computer vision, I do not believe several of the things you said are true. > > Pornographic images, for instance, are frequently identified not by the presence of particular body parts or structural features, but by the dominance of certain colors in the images > Neural networks This is definitely not what state of the art neural networks for computer vision do, although I'm sure there are many models out…

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Visualizing and Understanding Convolutional Networks — HN submission

Wed, 01 Oct 2014 22:32:16 GMT

Visualizing and Understanding Convolutional Networks

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