dogear

enter for all results · esc to close

Very Deep Convolutional Networks for Large-Scale Image Recognition

arxiv.orgsite

Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".

from
Education
added
2026-10-10
likes
0

similar

  1. Visualizing and Understanding Convolutional Networks arxiv.org

    For the "deconvnet layer".

  2. Going Deeper with Convolutions cv-foundation.org

    GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.

  3. ImageNet Classification with Deep Convolutional Neural Networks cs.toronto.edu

    AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function.

  4. Deep Convolutional Neural Network for Image Deconvolution lxu.me
  5. Scaling up Deep Learning videolectures.net
  6. Deep Residual Learning for Image Recognition arxiv.org

    Very deep residual layers with batch normalization layers - a.k.a. "how to overfit any vision dataset with too many layers and make any vision model work properly at recognition given enough data".

Education › Papers > Convolutional Neural Networks: “Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".”