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This paper deals with the question what / how exactly CNNs learn, considering the fact that they usually have more trainable parameters than data points on which they are trained. When the authors write "deep neural networks", they are talking about Inception V3, AlexNet and MLPs. ## Key contributions * Deep neural networks easily fit random labels (achieving a training error of 0 and a test error which is just randomly guessing labels as expected). $\Rightarrow$Those architectures can simply brute-force memorize the training data. * Deep neural networks fit random images (e.g. Gaussian noise) with 0 training error. The authors conclude that VC-dimension / Rademacher complexity, and uniform stability are bad explanations for generalization capabilities of neural networks * The authors give a construction for a 2-layer network with $p = 2n+d$ parameters - where $n$ is the number of samples and $d$ is the dimension of each sample - which can easily fit any labeling. (Finite sample expressivity). See section 4. ## What I learned * Any measure $m$ of the generalization capability of classifiers $H$ should take the percentage of corrupted labels ($p_c \in [0, 1]$, where $p_c =0$ is a perfect labeling and $p_c=1$ is totally random) into account: If $p_c = 1$, then $m()$ should be 0, too, as it is impossible to learn something meaningful with totally random labels. * We seem to have built models which work well on image data in general, but not "natural" / meaningful images as we thought. ## Funny > deep neural nets remain mysterious for many reasons > Note that this is not exactly simple as the kernel matrix requires 30GB to store in memory. Nonetheless, this system can be solved in under 3 minutes in on a commodity workstation with 24 cores and 256 GB of RAM with a conventional LAPACK call. ## See also * [Deep Nets Don't Learn Via Memorization](https://openreview.net/pdf?id=rJv6ZgHYg) |
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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 mini-batches of size 128. * ImageNet ILSVRC 2015: 3.57% (ensemble) * CIFAR-10: 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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This paper is about Convolutional Neural Networks for Computer Vision. It was the first break-through in the ImageNet classification challenge (LSVRC-2010, 1000 classes). ReLU was a key aspect which was not so often used before. The paper also used Dropout in the last two layers. ## Training details * Momentum of 0.9 * Learning rate of $\varepsilon$ (initialized at 0.01) * Weight decay of $0.0005 \cdot \varepsilon$. * Batch size of 128 * The training took 5 to 6 days on two NVIDIA GTX 580 3GB GPUs. ## See also * [Stanford presentation](http://vision.stanford.edu/teaching/cs231b_spring1415/slides/alexnet_tugce_kyunghee.pdf) |
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# Deep Convolutional Generative Adversarial Nets ## Introduction * The paper presents Deep Convolutional Generative Adversarial Nets (DCGAN) - a topologically constrained variant of conditional GAN. * [Link to the paper](https://arxiv.org/abs/1511.06434) ## Benefits * Stable to train * Very useful to learn unsupervised image representations. ## Model * GANs difficult to scale using CNNs. * Paper proposes following changes to GANs: * Replace any pooling layers with strided convolutions (for discriminator) and fractional strided convolutions (for generators). * Remove fully connected hidden layers. * Use batch normalisation in both generator (all layers except output layer) and discriminator (all layers except input layer). * Use LeakyReLU in all layers of the discriminator. * Use ReLU activation in all layers of the generator (except output layer which uses Tanh). ## Datasets * Large-Scale Scene Understanding. * Imagenet-1K. * Faces dataset. ## Hyperparameters * Minibatch SGD with minibatch size of 128. * Weights initialized with 0 centered Normal distribution with standard deviation = 0.02 * Adam Optimizer * Slope of leak = 0.2 for LeakyReLU. * Learning rate = 0.0002, β1 = 0.5 ## Observations * Large-Scale Scene Understanding data * Demonstrates that model scales with more data and higher resolution generation. * Even though it is unlikely that model would have memorized images (due to low learning rate of minibatch SGD). * Classifying CIFAR-10 dataset * Features * Train in Imagenet-1K and test on CIFAR-10. * Max pool discriminator's convolutional features (from all layers) to get 4x4 spatial grids. * Flatten and concatenate to get a 28672-dimensional vector. * Linear L2-SVM classifier trained over the feature vector. * 82.8% accuracy, outperforms K-means (80.6%) * Street View House Number Classifier * Similar pipeline as CIFAR-10 * 22.48% test error. * The paper contains many examples of images generated by final and intermediate layers of the network. * Images in the latent space do not show sharp transitions indicating that network did not memorize images. * DCGAN can learn an interesting hierarchy of features. * Networks seems to have some success in disentangling image representation from object representation. * Vector arithmetic can be performed on the Z vectors corresponding to the face samples to get results like `smiling woman - normal woman + normal man = smiling man` visually. |
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Mask RCNN takes off from where Faster RCNN left, with some augmentations aimed at bettering instance segmentation (which was out of scope for FRCNN). Instance segmentation was achieved remarkably well in *DeepMask* , *SharpMask* and later *Feature Pyramid Networks* (FPN). Faster RCNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is most evident in how RoIPool , the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. Mask RCNN fixes that by introducing RoIAlign in place of RoIPool. #### Methodology Mask RCNN retains most of the architecture of Faster RCNN. It adds the a third branch for segmentation. The third branch takes the output from RoIAlign layer and predicts binary class masks for each class. ##### Major Changes and intutions **Mask prediction** Mask prediction segmentation predicts a binary mask for each RoI using fully convolution - and the stark difference being usage of *sigmoid* activation for predicting final mask instead of *softmax*, implies masks don't compete with each other. This *decouples* segmentation from classification. The class prediction branch is used for class prediction and for calculating loss, the mask of predicted loss is used calculating Lmask. Also, they show that a single class agnostic mask prediction works almost as effective as separate mask for each class, thereby supporting their method of decoupling classification from segmentation **RoIAlign** RoIPool first quantizes a floating-number RoI to the discrete granularity of the feature map, this quantized RoI is then subdivided into spatial bins which are themselves quantized, and finally feature values covered by each bin are aggregated (usually by max pooling). Instead of quantization of the RoI boundaries or bin bilinear interpolation is used to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and aggregate the result (using max or average). **Backbone architecture** Faster RCNN uses a VGG like structure for extracting features from image, weights of which were shared among RPN and region detection layers. Herein, authors experiment with 2 backbone architectures - ResNet based VGG like in FRCNN and ResNet based [FPN](http://www.shortscience.org/paper?bibtexKey=journals/corr/LinDGHHB16) based. FPN uses convolution feature maps from previous layers and recombining them to produce pyramid of feature maps to be used for prediction instead of single-scale feature layer (final output of conv layer before connecting to fc layers was used in Faster RCNN) **Training Objective** The training objective looks like this ![](https://i.imgur.com/snUq73Q.png) Lmask is the addition from Faster RCNN. The method to calculate was mentioned above #### Observation Mask RCNN performs significantly better than COCO instance segmentation winners *without any bells and whiskers*. Detailed results are available in the paper |