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- ShortScience.org is a platform for post-publication discussion aiming to improve accessibility and reproducibility of research ideas.
- The website has 1584 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
- Reading summaries of papers is useful to obtain the perspective and insight of another reader, why they liked or disliked it, and their attempt to demystify complicated sections.
- Also, writing summaries is a good exercise to understand the content of a paper because you are forced to challenge your assumptions when explaining it.
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Adversarial Autoencoders

Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep and Goodfellow, Ian J.

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep and Goodfellow, Ian J.

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

[link]
#### Summary of this post: * an overview the motivation behind adversarial autoencoders and how they work * a discussion on whether the adversarial training is necessary in the first place. tl;dr: I think it's an overkill and I propose a simpler method along the lines of kernel moment matching. #### Adversarial Autoencoders Again, I recommend everyone interested to read the actual paper, but I'll attempt to give a high level overview the main ideas in the paper. I think the main figure from the paper does a pretty good job explaining how Adversarial Autoencoders are trained: ![](http://www.inference.vc/content/images/2016/01/Screen-Shot-2016-01-08-at-14-48-25.png) The top part of this image is a probabilistic autoencoder. Given the input $\mathbf{x}$, some latent code $\mathbf{z}$ is generated by sampling from an encoding distribution $q(\mathbf{z}\vert\mathbf{x})$. This distribution is typically modeled as the output a deep neural network. In normal autoencoders this encoder would be deterministic, now we allow it to be probabilistic. A decoder network is then trained to decode $\mathbf{z}$ and reconstruct the original input $\mathbf{x}$. Of course, reconstruction will not be perfect, but we train the networks to minimise reconstruction error, this is typically just mean squared error. The reconstruction cost ensures that the encoding process retains information about the input image, but it doesn't enforce anything else about what these latent representations $\mathbf{z}$ should do. In general, their distribution is described as the aggregate posterior $q(\mathbf{z})=\mathbb{E}_\mathbf{x} q(\mathbf{z}\vert\mathbf{x})$. Often, we would like this distribution to match a certain prior $p(\mathbf{z})$. For example. we may want $\mathbf{z}$ to have independent components and Gaussian distributed (nonlinear ICA,PCA). Or we may want to force the latent representations to correspond to discrete class labels, or binary factors. Or we may simply want to ensure there are 'no gaps' in the latent space, and any random $\mathbf{z}$ would lead to a viable sample when squashed through the decoder network. So there are multiple reasons why one might want to control the aggregate posterior $q(\mathbf{z})$ to match a predefined prior $p(\mathbf{z})$. The authors achieve this by introducing an additional term in the autoencoder loss function, one that measures the divergence between $q$ and $p$. The authors chose to do this via adversarial training: they train a discriminator network that constantly learns to discriminate between real code vectors $\mathbb{z}$ produced by encoding real data, and random code vectors sampled from $p$. If $q$ matches $p$ perfectly, the optimal discriminator network should have a large classification error. #### Is this an overkill? My main question about this paper was whether the adversarial cost is really needed here, because I think it's an overkill. Let me explain: Adversarial training is powerful when all else fails to quantify divergence between complicated, potentially degenerate distributions in high dimensions, such as images or video. Our toolkit for dealing with images is limited, CNNs are the best tool we have, so it makes sense to incorporate them in training generative models for images. GANs - when applied directly to images - are a great idea. However, here adversarial training is applied to an easier problem: to quantify the divergence between a simple, fixed prior (e.g. Gaussian) and an empirical distribution of latents. The latent space is usually lower-dimensional, distributions better behaved. Therefore, matching to $p(\mathbf{z})$ in latent space should be considerably easier than matching distributions over images. Adversarial training makes no assumptions about the distributions compared, other than sampling from them. This comes very handy when both $p$ and $q$ are nasty such as in the generative adversarial network scenario: there, $p$ is the distribution of natural images, $q$ is a super complicated, degenerate distribution produced by squashing noise through a deep convnet. The price we pay for this flexibility is this: when $p$ or $q$ are actually easy to work with, adversarial training cannot exploit that, it still has to sample. (it would be interesting to see if expectations over $p(\mathbf{z})$ could be computed analytically). So even though in this work $p$ is as simple as a mixture of ten 2D Gaussians, we need to approximate everything by drawing samples. #### Other things might work: kernel moment matching Why can’t one use easier divergences? For example, I think moment matching based on kernel MMD would work brilliantly in this scenario. It would have the following advantages over the adversarial cost. - closed form expressions: Depending on the choice of the prior $p(\mathbf{z})$ and kernel used in MMD, the expectations over $p$ may be available in closed form, without sampling. So for example if we use a squared exponential kernel and a mixture of Gaussians as $p$, the divergence from $p$ can be precomputed in closed form that is easy to evaluate. - no nasty inner loop: Adversarial training requires the discriminator network to be reoptimised every time the generative model changes. So we end up with a gradient descent in the inner loop of a gradient descent, which is anything but nice to work with. This is why it takes so long to get it working, the whole thing is pretty unstable. In contrast, to evaluate MMD, the inner loop is not needed. In fact, MMD can also be thought of as the solution to a convex maximisation problem, but via the kernel trick the maximum has a closed form solution. - the problem is well suited for MMD: because the distributions are smooth, and the space is nice and low-dimensional, MMD might work very well. Kernel-based methods struggle with complicated manifold-like structure of natural images, so I wouldn't expect MMD to be competitive with adversarial training if it is applied directly in the image space. Therefore, I actually prefer generative adversarial networks to generative moment matching networks. However, here we have an easier problem, simpler space, simpler distributions where MMD shines, and adversarial training is just not needed. |

Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, Joel and Güney, Fatma and Behl, Aseem and Geiger, Andreas

arXiv e-Print archive - 2017 via Local Bibsonomy

Keywords: dblp

Janai, Joel and Güney, Fatma and Behl, Aseem and Geiger, Andreas

arXiv e-Print archive - 2017 via Local Bibsonomy

Keywords: dblp

[link]
## Problems * **Computer Vision**: * Detection: Given a 2D image, where are cars, pedestrians, traffic signs? * Depth estimation: Given a 2D image, estimate the depth * **Planning**: Where do I want to go? * **Control**: How should I steer? ## Datasets * KITTI: Street segmentation (Computer Vision) * ISPRS * MOT * Cityscapes ## What I missed * GTSRB: The German Traffic Sign Recognition Benchmark dataset * GTSDB: The German Traffic Sign Detection Benchmark |

Fast R-CNN

Girshick, Ross B.

International Conference on Computer Vision - 2015 via Local Bibsonomy

Keywords: dblp

Girshick, Ross B.

International Conference on Computer Vision - 2015 via Local Bibsonomy

Keywords: dblp

[link]
This method is based on improving the speed of R-CNN \cite{conf/cvpr/GirshickDDM14} 1. Where R-CNN would have two different objective functions, Fast R-CNN combines localization and classification losses into a "multi-task 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 sub-windows of approximate size $h/H \times w/W$ and then max-pooling the values in each sub-window 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 R-CNN" \cite{conf/nips/RenHGS15} |

Feature Pyramid Networks for Object Detection

Lin, Tsung-Yi and Dollár, Piotr and Girshick, Ross B. and He, Kaiming and Hariharan, Bharath and Belongie, Serge J.

arXiv e-Print archive - 2016 via Local Bibsonomy

Keywords: dblp

Lin, Tsung-Yi and Dollár, Piotr and Girshick, Ross B. and He, Kaiming and Hariharan, Bharath and Belongie, Serge J.

arXiv e-Print archive - 2016 via Local Bibsonomy

Keywords: dblp

[link]
Feature Pyramid Networks (FPNs) build on top of the state-of-the-art implementation for object detection net - Faster RCNN. Faster RCNN faces a major problem in training for scale-invariance as the computations can be memory-intensive and extremely slow. So FRCNN only applies multi-scale approach while testing. On the other hand, feature pyramids were mainstream when hand-generated features were used -primarily to counter scale-invariance. Feature pyramids are collections of features computed at multi-scale versions of the same image. Improving on a similar idea presented in *DeepMask*, FPN brings back feature pyramids using different feature maps of conv layers with differing spatial resolutions with predictiosn happening on all levels of pyramid. Using feature maps directly as it is, would be tough as initial layers tend to contain lower level representations and poor semantics but good localisation whereas deeper layers tend to constitute higher level representations with rich semantics but suffer poor localisation due to multiple subsampling. ##### Methodology FPN can be used with any normal conv architecture, that's used for classification. In such an architecture all layers have progressively decreasing spatial resolutions (say C1, C2,..C5). FPN would now take C5 and convolve with 1x1 kernel to reduce filters to give P5. Next, P5 is upsampled and merged it to C4 (C4 is convolved with 1x1 kernel to decrease filter size in order to match that of upsampled P5) by adding element wise to produce P4. Similarly P4 is upsampled and merged with C3(in a similar way) to give P3 and so on. The final set of feature maps, in this case {P2 .. P5} are used as feature pyramids. This is how pyramids would look like ![](https://i.imgur.com/oHFmpww.png) *Usage of combination of {P2,..P5} as compared to only P2* : P2 produces highest resolution, most semantic features and could as well be the default choice but because of shared weights across rest of feature layers and the learned scale invariance makes the pyramidal variant more robust to generating false ROIs For next steps, it could be RPN or RCNN, the regression and classifier would share weights across for all *anchors* (of varying aspect ratios) at each level of the feature pyramids. This step is similar to [Single Shot Detector (SSD) Networks ](http://www.shortscience.org/paper?bibtexKey=conf/eccv/LiuAESRFB16) ##### Observation The FPN was used in FRCNN in both parts of RPN and RCNN separately and then combined FPN in both parts and produced state-of-the-art result in MS COCO challenges bettering results of COCO '15 & '16 winner models ( Faster RCNN +++ & GMRI) for mAP. FPN also can be used for instance segmentation by using fully convolutional layers on top of the image pyramids. FPN outperforms results from *DeepMask*, *SharpMask*, *InstanceFCN* |

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Gal, Yarin and Ghahramani, Zoubin

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

Gal, Yarin and Ghahramani, Zoubin

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

[link]
This paper presents an interpretation of dropout training as performing approximate Bayesian learning in a deep Gaussian process (DGP) model. This connection suggests a very simple way of obtaining, for networks trained with dropout, estimates of the model's output uncertainty. This estimate is based and computed from an ensemble of networks each obtained by sampling a new dropout mask. #### My two cents This is a really nice and thought provoking contribution to our understanding of dropout. Unfortunately, the paper in fact doesn't provide a lot of comparisons with either other ways of estimating the predictive uncertainty of deep networks, or to other approximate inference schemes in deep GPs (actually, see update below). The qualitative examples provided however do suggest that the uncertainty estimate isn't terrible. Irrespective of the quality of the uncertainty estimate suggested here, I find the observation itself really valuable. Perhaps future research will then shed light on how useful that method is compared to other approaches, including Bayesian dark knowledge \cite{conf/nips/BalanRMW15}. `Update: On September 27th`, the authors uploaded to arXiv a new version that now includes comparisons with 2 alternative Bayesian learning methods for deep networks, specifically the stochastic variational inference approach of Graves and probabilistic back-propagation of Hernandez-Lobato and Adams. Dropout actually does very well against these baselines and, across datasets, is almost always amongst the best performing method! |

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