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Convolutional neural networks
- 1. Roozbeh Sanaei Convolutional Neural Networks
- 2. 2 Convolution Roozbeh Sanaei Kernel = Filter = https://stackoverflow.com/questions/52067833/how-to-plot-an-animated-matrix-in-matplotlib
- 3. 3 Convolution Over Volumes Roozbeh Sanaei https://towardsdatascience.com/the-most-intuitive-and-easiest-guide-for-convolutional-neural-network-3607be47480
- 4. 4 Convolution Over Volumes Roozbeh Sanaei CNNs : Kernel = Filter = Feature Detector https://mmuratarat.github.io/2019-01-17/implementing-padding-schemes-of-tensorflow-in-python
- 6. 6 Strided Convolution Roozbeh Sanaei https://ai.stackexchange.com/questions/17004/convolutional-neural-network-does-each-filter-in-each-convolution-layer-create Stride-1 convolution ("non-strided") Stride-2 convolution ("strided")
- 7. 7 Convolution Operation Arithmetic Roozbeh Sanaei https://towardsdatascience.com/pytorch-basics-how-to-train-your-neural-net-intro-to-cnn-26a14c2ea29
- 8. 8 Max Pooling and Average Pooling Layers Roozbeh Sanaei Yani, Muhamad. "Application of transfer learning using convolutional neural network method for early detection of terry’s nail." Journal of Physics: Conference Series. Vol. 1201. No. 1. IOP Publishing, 2019.
- 9. 9 Why Convolutions? Roozbeh Sanaei Coursera Deep Learning Specialization, Convolutional Neural Networks Parameter Sharing: A feature detector (such as a vertical edge detector) that’s useful in one part of the image, is probably useful in another part of the image. Sparsity of Connections: In each layer, each output value depends only on small number of inputs.
- 10. 10 LeNet-5 Roozbeh Sanaei Yani, Muhamad. "Application of transfer learning using convolutional neural network method for early detection of terry’s nail." Journal of Physics: Conference Series. Vol. 1201. No. 1. IOP Publishing, 2019.
- 11. 11 Overlap Pooling Roozbeh Sanaei http://datahacker.rs/tf-alexnet/
- 14. 14 Roozbeh Sanaei https://realelectricwizard.wordpress.com/2019/03/30/the-mystery-of-vanishing-and-exploding-gradients/ Vanishing Gradient Problem
- 20. 20 Roozbeh Sanaei https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215 Spatially Separable Convolution
- 21. 21 Roozbeh Sanaei https://towardsdatascience.com/a-comprehensive-introduction-to-different-types-of-convolutions-in-deep-learning-669281e58215 Depthwise separable convolution
- 24. 24 Roozbeh Sanaei https://medium.com/analytics-vidhya/weakly-supervised-learning-for-object-localization-4b73d4f4f4a6 Object Classification, Localization, Detection, Segmentation
- 25. 25 Roozbeh Sanaei https://towardsdatascience.com/evolution-of-object-detection-and-localization-algorithms-e241021d8bad Object Classification and Localization
- 27. 27
- 29. 29 Roozbeh Sanaei https://www.analyticsvidhya.com/blog/2020/08/selecting-the-right-bounding-box-using-non-max-suppression-with-implementation/ Ground Truth Prediction Non-Max Suppression Step 1: Select the box with highest objectiveness score Step 2: Then, compare the overlap (intersection over union) of this box with other boxes Step 3: Remove the bounding boxes with overlap (intersection over union) >50% Step 4: Then, move to the next highest objectiveness score Step 5: Finally, repeat steps 2-4
- 30. 30 Roozbeh Sanaei https://towardsdatascience.com/region-proposal-network-a-detailed-view-1305c7875853 Ground Truth Prediction Anchor Points/Boxes
- 31. 31 1. Pre-train the CNN network on classification 2. Propose ROI candidates through selective search (class independent) 3. Warp ROIs candidates to CNN required size 4. Fine tune the CNN on warped ROIs for K + 1 classes (additional class is background) 5. CNN Generated features are fed to binary SVMs for each class 6. Regression model is trained to correct predicted window Roozbeh Sanaei RCNN
- 32. Roozbeh Sanaei 32 Generate region proposals that may contain objects 1. Create regions to start with through image segmentation 2. Iteratively: 1. Calculate similarities between all neighboring regions 2. Bundle two most similar regions 32 Roozbeh Sanaei Selective Search
- 33. Roozbeh Sanaei 33 Predicted bounding box coordinates Ground truth box coordinates So all the bounding box correction functions, can take any value between [-∞, +∞]. 33 Roozbeh Sanaei Bounding Box Regression
- 34. Roozbeh Sanaei 34 • Finding false positive samples during the training loops • Including them in the training data so as to improve the classifier. 34 Roozbeh Sanaei Hard Negative Mining
- 35. Roozbeh Sanaei 35 Instead of extracting separate CNN feature vectors for each region proposal, they are aggregated into one CNN forward pass over the entire image, So pretrained CNN is altered as: • The last max pooling layer of the pre-trained CNN is replaced with a RoI pooling layer. • The last fully connected layer and the last softmax layer (K classes) with a fully connected layer and softmax over K + 1 classes. Branch the last layer out to a bounding-box regression model which predicts offsets relative to the original RoI for each of K classes. 35 Roozbeh Sanaei Faster RCNN
- 36. Roozbeh Sanaei 36 The loss function sums up the cost of classification and bounding box prediction where bounding box prediction is ignored for the “background” 36 Roozbeh Sanaei RCNN Loss function
- 37. Roozbeh Sanaei 37 https://deepsense.ai/region-of-interest-pooling-explained/ 37 Roozbeh Sanaei ROI Pooling
- 38. Roozbeh Sanaei 38 https://arxiv.org/pdf/1611.10012.pdf 38 Roozbeh Sanaei Region Proposal Network
- 39. Roozbeh Sanaei 39 https://towardsdatascience.com/region-proposal-network-a-detailed-view-1305c7875853 Anchor point: Every point in the feature map generated by backbone network Anchor boxes: Candidate boxes generated for every anchor point with different scale and aspect ratio 39 Roozbeh Sanaei Anchor Points/Boxes
- 40. Roozbeh Sanaei 40 https://towardsdatascience.com/region-proposal-network-a-detailed-view-1305c7875853 40 Roozbeh Sanaei Region Proposal Network (RPN)
- 41. Roozbeh Sanaei 41 https://towardsdatascience.com/region-proposal-network-a-detailed-view-1305c7875853 41 Roozbeh Sanaei Full Network Architecture
- 42. Roozbeh Sanaei 42 file:///C:/Users/uic81403/Downloads/03fasterr-cnn-towardsreal-timeobjectdetectionwithregionproposalnetworks-160301150110.pdf Roozbeh Sanaei 42 RPN Loss
- 43. Roozbeh Sanaei 43 (SSD) 43 Roozbeh Sanaei Feature Pyramid
- 44. Roozbeh Sanaei 44 Reconstructed layers are semantically strong but localization is inaccurate due to down-sampling and up-sampling. Lateral connections between reconstructed layers and the corresponding feature maps improves localization 44 Roozbeh Sanaei Feature Pyramid
- 45. Roozbeh Sanaei 45 45 Roozbeh Sanaei Transposed Convolution https://towardsdatascience.com/transposed-convolution-demystified-84ca81b4baba
- 46. Roozbeh Sanaei 46 46 Roozbeh Sanaei UNet https://towardsdatascience.com/unet-line-by-line-explanation-9b191c76baf5
- 47. Roozbeh Sanaei 47 47 Roozbeh Sanaei Siamese network and binary classification https://www.pyimagesearch.com/2020/11/30/siamese-networks-with-keras-tensorflow-and-deep-learning/
- 48. Roozbeh Sanaei 48 48 Roozbeh Sanaei Siamese network and triplet loss https://omoindrot.github.io/triplet-loss
- 49. Roozbeh Sanaei 49 49 Roozbeh Sanaei Gram Matrix https://towardsdatascience.com/introduction-to-neural-style-transfer-with-tensorflow-99915a5d624a
- 50. Roozbeh Sanaei 50 50 Roozbeh Sanaei Style Transfer https://towardsdatascience.com/introduction-to-neural-style-transfer-with-tensorflow-99915a5d624a Content Style Generated Image G represents gram matrix