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This method is based on improving the speed of RCNN \cite{conf/cvpr/GirshickDDM14} 1. Where RCNN would have two different objective functions, Fast RCNN combines localization and classification losses into a "multitask loss" in order to speed up training. 2. It also uses a pooling method based on \cite{journals/pami/HeZR015} called the RoI pooling layer that scales the input so the images don't have to be scaled before being set an an input image to the CNN. "RoI max pooling works by dividing the $h \times w$ RoI window into an $H \times W$ grid of subwindows of approximate size $h/H \times w/W$ and then maxpooling the values in each subwindow into the corresponding output grid cell." 3. Backprop is performed for the RoI pooling layer by taking the argmax of the incoming gradients that overlap the incoming values. This method is further improved by the paper "Faster RCNN" \cite{conf/nips/RenHGS15} 
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Deeper networks should never have a higher **training** error than smaller ones. In the worst case, the layers should "simply" learn identities. It seems as this is not so easy with conventional networks, as they get much worse with more layers. So the idea is to add identity functions which skip some layers. The network only has to learn the **residuals**. Advantages: * Learning the identity becomes learning 0 which is simpler * Loss in information flow in the forward pass is not a problem anymore * No vanishing / exploding gradient * Identities don't have parameters to be learned ## Evaluation The learning rate starts at 0.1 and is divided by 10 when the error plateaus. Weight decay of 0.0001 ($10^{4}$), momentum of 0.9. They use minibatches of size 128. * ImageNet ILSVRC 2015: 3.57% (ensemble) * CIFAR10: 6.43% * MS COCO: 59.0% mAp@0.5 (ensemble) * PASCAL VOC 2007: 85.6% mAp@0.5 * PASCAL VOC 2012: 83.8% mAp@0.5 ## See also * [DenseNets](http://www.shortscience.org/paper?bibtexKey=journals/corr/1608.06993) 
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# Object detection system overview. https://i.imgur.com/vd2YUy3.png 1. takes an input image, 2. extracts around 2000 bottomup region proposals, 3. computes features for each proposal using a large convolutional neural network (CNN), and then 4. classifies each region using classspecific linear SVMs. * RCNN achieves a mean average precision (mAP) of 53.7% on PASCAL VOC 2010. * On the 200class ILSVRC2013 detection dataset, RCNN’s mAP is 31.4%, a large improvement over OverFeat , which had the previous best result at 24.3%. ## There is a 2 challenges faced in object detection 1. localization problem 2. labeling the data 1 localization problem : * One approach frames localization as a regression problem. they report a mAP of 30.5% on VOC 2007 compared to the 58.5% achieved by our method. * An alternative is to build a slidingwindow detector. considered adopting a slidingwindow approach increases the number of convolutional layers to 5, have very large receptive fields (195 x 195 pixels) and strides (32x32 pixels) in the input image, which makes precise localization within the slidingwindow paradigm. 2 labeling the data: * The conventional solution to this problem is to use unsupervised pretraining, followed by supervise finetuning * supervised pretraining on a large auxiliary dataset (ILSVRC), followed by domain specific finetuning on a small dataset (PASCAL), * finetuning for detection improves mAP performance by 8 percentage points. * Stochastic gradient descent via back propagation was used to effective for training convolutional neural networks (CNNs) ## Object detection with RCNN This system consists of three modules * The first generates categoryindependent region proposals. These proposals define the set of candidate detections available to our detector. * The second module is a large convolutional neural network that extracts a fixedlength feature vector from each region. * The third module is a set of class specific linear SVMs. Module design 1 Region proposals * which detect mitotic cells by applying a CNN to regularlyspaced square crops. * use selective search method in fast mode (Capture All Scales, Diversification, Fast to Compute). * the time spent computing region proposals and features (13s/image on a GPU or 53s/image on a CPU) 2 Feature extraction. * extract a 4096dimensional feature vector from each region proposal using the Caffe implementation of the CNN * Features are computed by forward propagating a meansubtracted 227x227 RGB image through five convolutional layers and two fully connected layers. * warp all pixels in a tight bounding box around it to the required size * The feature matrix is typically 2000x4096 3 Test time detection * At test time, run selective search on the test image to extract around 2000 region proposals (we use selective search’s “fast mode” in all experiments). * warp each proposal and forward propagate it through the CNN in order to compute features. Then, for each class, we score each extracted feature vector using the SVM trained for that class. * Given all scored regions in an image, we apply a greedy nonmaximum suppression (for each class independently) that rejects a region if it has an intersectionover union (IoU) overlap with a higher scoring selected region larger than a learned threshold. ## Training 1 Supervised pretraining: * pretrained the CNN on a large auxiliary dataset (ILSVRC2012 classification) using imagelevel annotations only (bounding box labels are not available for this data) 2 Domainspecific finetuning. * use the stochastic gradient descent (SGD) training of the CNN parameters using only warped region proposals with learning rate of 0.001. 3 Object category classifiers. * use intersectionover union (IoU) overlap threshold method to label a region with The overlap threshold of 0.3. * Once features are extracted and training labels are applied, we optimize one linear SVM per class. * adopt the standard hard negative mining method to fit large training data in memory. ### Results on PASCAL VOC 201012 1 VOC 2010 * compared against four strong baselines including SegDPM, DPM, UVA, Regionlets. * Achieve a large improvement in mAP, from 35.1% to 53.7% mAP, while also being much faster https://i.imgur.com/0dGX9b7.png 2 ILSVRC2013 detection. * ran RCNN on the 200class ILSVRC2013 detection dataset * RCNN achieves a mAP of 31.4% https://i.imgur.com/GFbULx3.png #### Performance layerbylayer, without finetuning 1 pool5 layer * which is the max pooled output of the network’s fifth and final convolutional layer. *The pool5 feature map is 6 x6 x 256 = 9216 dimensional * each pool5 unit has a receptive field of 195x195 pixels in the original 227x227 pixel input 2 Layer fc6 * fully connected to pool5 * it multiplies a 4096x9216 weight matrix by the pool5 feature map (reshaped as a 9216dimensional vector) and then adds a vector of biases 3 Layer fc7 * It is implemented by multiplying the features computed by fc6 by a 4096 x 4096 weight matrix, and similarly adding a vector of biases and applying halfwave rectification #### Performance layerbylayer, with finetuning * CNN’s parameters finetuned on PASCAL. * finetuning increases mAP by 8.0 % points to 54.2% ### Network architectures * 16layer deep network, consisting of 13 layers of 3 _ 3 convolution kernels, with five max pooling layers interspersed, and topped with three fullyconnected layers. We refer to this network as “ONet” for OxfordNet and the baseline as “TNet” for TorontoNet. * RCNN with ONet substantially outperforms RCNN with TNet, increasing mAP from 58.5% to 66.0% * drawback in terms of compute time, with in terms of compute time, with than TNet. 1 The ILSVRC2013 detection dataset * dataset is split into three sets: train (395,918), val (20,121), and test (40,152) #### CNN features for segmentation. * full RCNN: The first strategy (full) ignores the re region’s shape and computes CNN features directly on the warped window. Two regions might have very similar bounding boxes while having very little overlap. * fg RCNN: the second strategy (fg) computes CNN features only on a region’s foreground mask. We replace the background with the mean input so that background regions are zero after mean subtraction. * full+fg RCNN: The third strategy (full+fg) simply concatenates the full and fg features https://i.imgur.com/n1bhmKo.png
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This very new paper, is currently receiving quite a bit of attention by the [community](https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/). The paper describes a new training approach, which solves the two major practical problems with current GAN training: 1) The training process comes with a meaningful loss. This can be used as a (soft) performance metric and will help debugging, tune parameters and so on. 2) The training process does not suffer from all the instability problems. In particular the paper reduces mode collapse significantly. On top of that, the paper comes with quite a bit mathematical theory, explaining why there approach works and other approachs have failed. This paper is a must read for anyone interested in GANs. 
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The main contribution of this paper is introducing a new transformation that the authors call Batch Normalization (BN). The need for BN comes from the fact that during the training of deep neural networks (DNNs) the distribution of each layer’s input change. This phenomenon is called internal covariate shift (ICS). #### What is BN? Normalize each (scalar) feature independently with respect to the mean and variance of the mini batch. Scale and shift the normalized values with two new parameters (per activation) that will be learned. The BN consists of making normalization part of the model architecture. #### What do we gain? According to the author, the use of BN provides a great speed up in the training of DNNs. In particular, the gains are greater when it is combined with higher learning rates. In addition, BN works as a regularizer for the model which allows to use less dropout or less L2 normalization. Furthermore, since the distribution of the inputs is normalized, it also allows to use sigmoids as activation functions without the saturation problem. #### What follows? This seems to be specially promising for training recurrent neural networks (RNNs). The vanishing and exploding gradient problems \cite{journals/tnn/BengioSF94} have their origin in the iteration of transformation that scale up or down the activations in certain directions (eigenvectors). It seems that this regularization would be specially useful in this context since this would allow the gradient to flow more easily. When we unroll the RNNs, we usually have ultra deep networks. #### Like * Simple idea that seems to improve training. * Makes training faster. * Simple to implement. Probably. * You can be less careful with initialization. #### Dislike * Does not work with stochastic gradient descent (minibatch size = 1). * This could reduce the parallelism of the algorithm since now all the examples in a mini batch are tied. * Results on ensemble of networks for ImageNet makes it harder to evaluate the relevance of BN by itself. (Although they do mention the performance of a single model). 