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* Usually GANs transform a noise vector `z` into images. `z` might be sampled from a normal or uniform distribution. * The effect of this is, that the components in `z` are deeply entangled. * Changing single components has hardly any influence on the generated images. One has to change multiple components to affect the image. * The components end up not being interpretable. Ideally one would like to have meaningful components, e.g. for human faces one that controls the hair length and a categorical one that controls the eye color. * They suggest a change to GANs based on Mutual Information, which leads to interpretable components. * E.g. for MNIST a component that controls the stroke thickness and a categorical component that controls the digit identity (1, 2, 3, ...). * These components are learned in a (mostly) unsupervised fashion. ### How * The latent code `c` * "Normal" GANs parameterize the generator as `G(z)`, i.e. G receives a noise vector and transforms it into an image. * This is changed to `G(z, c)`, i.e. G now receives a noise vector `z` and a latent code `c` and transforms both into an image. * `c` can contain multiple variables following different distributions, e.g. in MNIST a categorical variable for the digit identity and a gaussian one for the stroke thickness. * Mutual Information * If using a latent code via `G(z, c)`, nothing forces the generator to actually use `c`. It can easily ignore it and just deteriorate to `G(z)`. * To prevent that, they force G to generate images `x` in a way that `c` must be recoverable. So, if you have an image `x` you must be able to reliable tell which latent code `c` it has, which means that G must use `c` in a meaningful way. * This relationship can be expressed with mutual information, i.e. the mutual information between `x` and `c` must be high. * The mutual information between two variables X and Y is defined as `I(X; Y) = entropy(X)  entropy(XY) = entropy(Y)  entropy(YX)`. * If the mutual information between X and Y is high, then knowing Y helps you to decently predict the value of X (and the other way round). * If the mutual information between X and Y is low, then knowing Y doesn't tell you much about the value of X (and the other way round). * The new GAN loss becomes `old loss  lambda * I(G(z, c); c)`, i.e. the higher the mutual information, the lower the result of the loss function. * Variational Mutual Information Maximization * In order to minimize `I(G(z, c); c)`, one has to know the distribution `P(cx)` (from image to latent code), which however is unknown. * So instead they create `Q(cx)`, which is an approximation of `P(cx)`. * `I(G(z, c); c)` is then computed using a lower bound maximization, similar to the one in variational autoencoders (called "Variational Information Maximization", hence the name "InfoGAN"). * Basic equation: `LowerBoundOfMutualInformation(G, Q) = E[log Q(cx)] + H(c) <= I(G(z, c); c)` * `c` is the latent code. * `x` is the generated image. * `H(c)` is the entropy of the latent codes (constant throughout the optimization). * Optimization w.r.t. Q is done directly. * Optimization w.r.t. G is done via the reparameterization trick. * If `Q(cx)` approximates `P(cx)` *perfectly*, the lower bound becomes the mutual information ("the lower bound becomes tight"). * In practice, `Q(cx)` is implemented as a neural network. Both Q and D have to process the generated images, which means that they can share many convolutional layers, significantly reducing the extra cost of training Q. ### Results * MNIST * They use for `c` one categorical variable (10 values) and two continuous ones (uniform between 1 and +1). * InfoGAN learns to associate the categorical one with the digit identity and the continuous ones with rotation and width. * Applying Q(cx) to an image and then classifying only on the categorical variable (i.e. fully unsupervised) yields 95% accuracy. * Sampling new images with exaggerated continuous variables in the range `[2,+2]` yields sound images (i.e. the network generalizes well). * ![MNIST examples](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/InfoGAN__mnist.png?raw=true "MNIST examples") * 3D face images * InfoGAN learns to represent the faces via pose, elevation, lighting. * They used five uniform variables for `c`. (So two of them apparently weren't associated with anything sensible? They are not mentioned.) * 3D chair images * InfoGAN learns to represent the chairs via identity (categorical) and rotation or width (apparently they did two experiments). * They used one categorical variable (four values) and one continuous variable (uniform `[1, +1]`). * SVHN * InfoGAN learns to represent lighting and to spot the center digit. * They used four categorical variables (10 values each) and two continuous variables (uniform `[1, +1]`). (Again, a few variables were apparently not associated with anything sensible?) * CelebA * InfoGAN learns to represent pose, presence of sunglasses (not perfectly), hair style and emotion (in the sense of "smiling or not smiling"). * They used 10 categorical variables (10 values each). (Again, a few variables were apparently not associated with anything sensible?) * ![CelebA examples](https://raw.githubusercontent.com/aleju/papers/master/neuralnets/images/InfoGAN__celeba.png?raw=true "CelebA examples") 
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This paper introduces a new AI task  Embodied Question Answering. The goal of this task for an agent is to be able to answer the question by observing the environment through a single egocentric RGB camera while being able to navigate inside the environment. The agent has 4 natural modules: https://i.imgur.com/6Mjidsk.png 1. **Vision**. 224x224 RGB images are processed by CNN to produce a fixedsize representation. This CNN is pretrained on pixeltopixel tasks such as RGB reconstruction, semantic segmentation, and depth estimation. 2. **Language**. Questions are encoded with 2layer LSTMs with 128d hidden states. Separate question encoders are used for the navigation and answering modules to capture important words for each module. 3. **Navigation** is composed of a planner (forward, left, right, and stop actions) and a controller that executes planner selected action for a variable number of times. The planner is LSTM taking hidden state, image representation, question, and previous action. Contrary, a controller is an MLP with 1 hidden layer which takes planner's hidden state, action from the planner, and image representation to execute an action or pass the lead back to the planner. 4. **Answering** module computes an imagequestion similarity of the last 5 frames via a dot product between image features (passed through an fclayer to align with question features) and question encoding. This similarity is converted to attention weights via a softmax, and the attentionweighted image features are combined with the question features and passed through an answer classifier. Visually this process is shown in the figure below. https://i.imgur.com/LeZlSZx.png [Successful results](https://www.youtube.com/watch?v=gVjTeIJfrk) as well as [failure cases](https://www.youtube.com/watch?v=4zH8cz2VlEg) are provided. Generally, this is very promising work which literally just scratches the surface of what is possible. There are several constraints which can be mitigated to push this field to more general outcomes. For example, use more general environments with more realistic graphics and broader set of questions and answers. 
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This paper describes how to apply the idea of batch normalization (BN) successfully to recurrent neural networks, specifically to LSTM networks. The technique involves the 3 following ideas: **1) Careful initialization of the BN scaling parameter.** While standard practice is to initialize it to 1 (to have unit variance), they show that this situation creates problems with the gradient flow through time, which vanishes quickly. A value around 0.1 (used in the experiments) preserves gradient flow much better. **2) Separate BN for the "hiddens to hiddens preactivation and for the "inputs to hiddens" preactivation.** In other words, 2 separate BN operators are applied on each contributions to the preactivation, before summing and passing through the tanh and sigmoid nonlinearities. **3) Use of largest timestep BN statistics for longer testtime sequences.** Indeed, one issue with applying BN to RNNs is that if the input sequences have varying length, and if one uses pertimestep mean/variance statistics in the BN transformation (which is the natural thing to do), it hasn't been clear how do deal with the last time steps of longer sequences seen at test time, for which BN has no statistics from the training set. The paper shows evidence that the preactivation statistics tend to gradually converge to stationary values over time steps, which supports the idea of simply using the training set's last time step statistics. Among these ideas, I believe the most impactful idea is 1). The papers mentions towards the end that improper initialization of the BN scaling parameter probably explains previous failed attempts to apply BN to recurrent networks. Experiments on 4 datasets confirms the method's success. **My two cents** This is an excellent development for LSTMs. BN has had an important impact on our success in training deep neural networks, and this approach might very well have a similar impact on the success of LSTMs in practice. 
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This paper is about a recommendation system approach using collaborative filtering (CF) on implicit feedback datasets. The core of it is the minimization problem $$\min_{x_*, y_*} \sum_{u,i} c_{ui} (p_{ui}  x_u^T y_i)^2 + \underbrace{\lambda \left ( \sum_u  x_u ^2 + \sum_i  y_i ^2\right )}_{\text{Regularization}}$$ with * $\lambda \in [0, \infty[$ is a hyper parameter which defines how strong the model is regularized * $u$ denoting a user, $u_*$ are all user factors $x_u$ combined * $i$ denoting an item, $y_*$ are all item factors $y_i$ combined * $x_u \in \mathbb{R}^n$ is the latent user factor (embedding); $n$ is another hyper parameter. $n=50$ seems to be a reasonable choice. * $y_i \in \mathbb{R}^n$ is the latent item factor (embedding) * $r_{ui}$ defines the "intensity"; higher values mean user $u$ interacted more with item $i$ * $p_{ui} = \begin{cases}1 & \text{if } r_{ui} >0\\0 &\text{otherwise}\end{cases}$ * $c_{ui} := 1 + \alpha r_{ui}$ where $\alpha \in [0, \infty[$ is a hyper parameter; $\alpha =40$ seems to be reasonable In contrast, the standard matrix factoriation optimization function looks like this ([example](https://www.cs.cmu.edu/~mgormley/courses/10601s17/slides/lecture25mf.pdf)): $$\min_{x_*, y_*} \sum_{(u, i, r_{ui}) \in \mathcal{R}} {(r_{ui}  x_u^T y_i)}^2 + \underbrace{\lambda \left ( \sum_u  x_u ^2 + \sum_i  y_i ^2\right )}_{\text{Regularization}}$$ where * $\mathcal{R}$ is the set of all ratings $(u, i, r_{ui})$  user $u$ has rated item $i$ with value $r_{ui} \in \mathbb{R}$ They use alternating least squares (ALS) to train this model. The prediction then is the dot product between the user factor and all item factors ([source](https://github.com/benfred/implicit/blob/master/implicit/recommender_base.pyx#L157L176)) 
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This paper describes how to find local interpretable modelagnostic explanations (LIME) why a blackbox model $m_B$ came to a classification decision for one sample $x$. The key idea is to evaluate many more samples around $x$ (local) and fit an interpretable model $m_I$ to it. The way of sampling and the kind of interpretable model depends on the problem domain. For computer vision / image classification, the image $x$ is divided into superpixels. Single superpixels are made black, the new image $x'$ is evaluated $p' = m_B(x')$. This is done multiple times. The paper is also explained in [this YouTube video](https://www.youtube.com/watch?v=KP7JtFMLo4) by Marco Tulio Ribeiro. A very similar idea is already in the [Zeiler & Fergus paper](http://www.shortscience.org/paper?bibtexKey=journals/corr/ZeilerF13#martinthoma). ## Followup Paper * June 2016: [ModelAgnostic Interpretability of Machine Learning](https://arxiv.org/abs/1606.05386) * November 2016: * [Nothing Else Matters: ModelAgnostic Explanations By Identifying Prediction Invariance](https://arxiv.org/abs/1611.05817) * [An unexpected unity among methods for interpreting model predictions](https://arxiv.org/abs/1611.07478) 
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One of the dominant narratives of the deep learning renaissance has been the value of welldesigned inductive bias  structural choices that shape what a model learns. The biggest example of this can be found in convolutional networks, where models achieve a dramatic parameter reduction by having features maps learn local patterns, which can then be reused across the whole image. This is based on the prior belief that patterns in local images are generally locally contiguous, and so having feature maps that focus only on small (and gradually larger) local areas is a good fit for that prior. This paper operates in a similar spirit, except its input data isn’t in the form of an image, but a graph: the social graph of multiple agents operating within a Multi Agent RL Setting. In some sense, a graph is just a more general form of a pixel image: where a pixel within an image has a fixed number of neighbors, which have fixed discrete relationships to it (up, down, left, right), nodes within graphs have an arbitrary number of nodes, which can have arbitrary numbers and types of attributes attached to that relationship. The authors of this paper use graph networks as a sort of auxiliary information processing system alongside a more typical policy learning framework, on tasks that require group coordination and knowledge sharing to complete successfully. For example, each agent might be rewarded based on the aggregate reward of all agents together, and, in the stag hunt, it might require collaborative effort by multiple agents to successfully “capture” a reward. Because of this, you might imagine that it would be valuable to be able to predict what other agents within the game are going to do under certain circumstances, so that you can shape your strategy accordingly. The graph network used in this model represents both agents and objects in the environment as nodes, which have attributes including their position, whether they’re available or not (for captureable objects), and what their last action was. As best I can tell, all agents start out with directed connections going both ways to all other agents, and to all objects in the environment, with the only edge attribute being whether the players are on the same team, for competitive environments. Given this setup, the graph network works through a sort of “diffusion” of information, analogous to a message passing algorithm. At each iteration (analogous to a layer), the edge features pull in information from their past value and sender and receiver nodes, as well as from a “global feature”. Then, all of the nodes pull in information from their edges, and their own past value. Finally, this “global attribute” gets updated based on summations over the newlyupdated node and edge information. (If you were predicting attributes that were graphlevel attributes, this global attribute might be where you’d do that prediction. However, in this case, we’re just interested in predicting agentlevel actions). https://i.imgur.com/luFlhfJ.png All of this has the effect of explicitly modeling agents as entities that both have information, and have connections to other entities. One benefit the authors claim of this structure is that it allows them more interpretability: when they “play out” the values of their graph network, which they call a Relational Forward Model or RFM, they observe edge values for two agents go up if those agents are about to collaborate on an action, and observe edge values for an agent and an object go up before that object is captured. Because this information is carefully shaped and structured, it makes it easier for humans to understand, and, in the tests the authors ran, appears to also help agents do better in collaborative games. https://i.imgur.com/BCKSmIb.png While I find graph networks quite interesting, and multiagent learning quite interesting, I’m a little more uncertain about the inherent “graphiness” of this problem, since there aren’t really meaningful inherent edges between agents. One thing I am curious about here is how methods like these would work in situations of sparser graphs, or, places where the connectivity level between a node’s neighbors, and the average other node in the graph is more distinct. Here, every node is connected to every other node, so the explicit information localization function of graph networks is less pronounced. I might naively think that  to whatever extent the graph is designed in a way that captures information meaningful to the task  explicit graph methods would have an even greater comparative advantage in this setting. 
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## General stuff about face recognition Face recognition has 4 main tasks: * **Face detection**: Given an image, draw a rectangle around every face * **Face alignment**: Transform a face to be in a canonical pose * **Face representation**: Find a representation of a face which is suitable for followup tasks (small size, computationally cheap to compare, invariant to irrelevant changes) * **Face verification**: Images of two faces are given. Decide if it is the same person or not. The face verification task is sometimes (more simply) a face classification task (given a face, decide which of a fixed set of people it is). Datasets being used are: * **LFW** (Labeled Faces in the Wild): 97.35% accuracy; 13 323 web photos of 5 749 celebrities * **YTF** (YouTube Faces): 3425 YouTube videos of 1 595 subjects * **SFC** (Social Face Classification): 4.4 million labeled faces from 4030 people, each 800 to 1200 faces * **USF** (HumanID database): 3D scans of faces ## Ideas in this paper This paper deals with face alignment and face representation. **Face Alignment** They made an average face with the USF dataset. Then, for each new face, they apply the following procedure: * Find 6 points in a face (2 eyes, 1 nose tip, 2 corners of the lip, 1 middle point of the bottom lip) * Crop according to those * Find 67 points in the face / apply them to a normalized 3D model of a face * Transform (=align) face to a normalized position **Representation** Train a neural network on 152x152 images of faces to classify 4030 celebrities. Remove the softmax output layer and use the output of the secondlast layer as the transformed representation. The network is: * C1 (convolution): 32 filters of size $11 \times 11 \times 3$ (RGBchannels) (returns $142\times 142$ "images") * M2 (max pooling): $3 \times 3$, stride of 2 (returns $71\times 71$ "images") * C3 (convolution): 16 filters of size $9 \times 9 \times 16$ (returns $63\times 63$ "images") * L4 (locally connected): $16\times9\times9\times16$ (returns $55\times 55$ "images") * L5 (locally connected): $16\times7\times7\times16$ (returns $25\times 25$ "images") * L6 (locally connected): $16\times5\times5\times16$ (returns $21\times 21$ "images") * F7 (fully connected): ReLU, 4096 units * F8 (fully connected): softmax layer with 4030 output neurons The training was done with: * Stochastic Gradient Descent (SGD) * Momentum of 0.9 * Performance scheduling (LR starting at 0.01, ending at 0.0001) * Weight initialization: $w \sim \mathcal{N}(\mu=0, \sigma=0.01)$, $b = 0.5$ * ~15 epochs ($\approx$ 3 days) of training ## Evaluation results * **Quality**: * 97.35% accuracy (or mean accuracy?) with an Ensemble of DNNs for LFW * 91.4% accuracy with a single network on YTF * **Speed**: DeepFace runs in 0.33 seconds per image (I'm not sure which size). This includes image decoding, face detection and alignment, **the** feed forward network (why only one? wasn't this the best performing Ensemble?) and final classification output ## See also * Andrew Ng: [C4W4L03 Siamese Network](https://www.youtube.com/watch?v=6jfw8MuKwpI) 
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A paper in the intersection for Computer Vision and Machine Learning. They propose a method (network in network) to reduce parameters. Essentially, it boils down to a pattern of (conv with size > 1) > (1x1 conv) > (1x1 conv) > repeat ## Datasets stateoftheart classification performances with NIN on CIFAR10 and CIFAR100, and reasonable performances on SVHN and MNIST ## Implementations * [Lasagne](https://github.com/Lasagne/Recipes/blob/master/modelzoo/cifar10_nin.py) 
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Spatial Pyramid Pooling (SPP) is a technique which allows Convolutional Neural Networks (CNNs) to use input images of any size, not only $224\text{px} \times 224\text{px}$ as most architectures do. (However, there is a lower bound for the size of the input image). ## Idea * Convolutional layers operate on any size, but fully connected layers need fixedsize inputs * Solution: * Add a new SPP layer on top of the last convolutional layer, before the fully connected layer * Use an approach similar to bag of words (BoW), but maintain the spatial information. The BoW approach is used for text classification, where the order of the words is discarded and only the number of occurences is kept. * The SPP layer operates on each feature map independently. * The output of the SPP layer is of dimension $k \cdot M$, where $k$ is the number of feature maps the SPP layer got as input and $M$ is the number of bins. Example: We could use spatial pyramid pooling with 21 bins: * 1 bin which is the max of the complete feature map * 4 bins which divide the image into 4 regions of equal size (depending on the input size) and rectangular shape. Each bin gets the max of its region. * 16 bins which divide the image into 4 regions of equal size (depending on the input size) and rectangular shape. Each bin gets the max of its region. ## Evaluation * Pascal VOC 2007, Caltech101: stateoftheart, without finetuning * ImageNet 2012: Boosts accuracy for various CNN architectures * ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014: Rank #2 ## Code The paper claims that the code is [here](http://research.microsoft.com/enus/um/people/kahe/), but this seems not to be the case any more. People have tried to implement it with Tensorflow ([1](http://stackoverflow.com/q/40913794/562769), [2](https://github.com/fchollet/keras/issues/2080), [3](https://github.com/tensorflow/tensorflow/issues/6011)), but by now no public working implementation is available. ## Related papers * [Atrous Convolution](https://arxiv.org/abs/1606.00915)
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### Main Idea: It basically tunes the hyperparameters of the neural network architecture using reinforcement learning. The reward signal is taken as evaluation on the validation set. The method is policy gradient as the cost function is nondifferentiable. ### Method: #### i. Actions: 1. There is controller RNN which predicts some hyperparameters of the layer conditioned on the previous predictions. This prediction is just a onehot vector based on the previous hyperparameter chosen. At the start for the first prediction  this vector is just all zeros. 2. Once the network is generated completely, it is trained for a fixed number of epochs, the reward signal is calculated based on the evaluation on the validation set. #### ii. Training: 1. Nothing fancy in the reinforcement learning approach simple policy gradients. 2. Baseline is added to reduce the variance. 3. It takes 23 weeks to train it over 800 GPUs!! #### iii. Results: 1. Use it to generate CNNs and LSTM cells. Close to stateofart results with generated architectures. ### Possible new directions: 1. Use better techniques RL techniques like TRPO, PPO etc. 2. Right now, they generate fixed length architecture. Their reason is for variable length architectures, it is difficult to determine how much time each architecture is trained. Smaller networks are easier to train. Thus, somehow determine training time as function of the learning capacity of the network. Code: They haven't released the code yet. I tried to simulate it in torch.(https://github.com/abhigenie92/nn_search) 