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**Object detection** is the task of drawing one bounding box around each instance of the type of object one wants to detect. Typically, image classification is done before object detection. With neural networks, the usual procedure for object detection is to train a classification network, replace the last layer with a regression layer which essentially predicts pixelwise if the object is there or not. An bounding box inference algorithm is added at last to make a consistent prediction (see [Deep Neural Networks for Object Detection](http://papers.nips.cc/paper/5207deepneuralnetworksforobjectdetection.pdf)). The paper introduces RPNs (Region Proposal Networks). They are endtoend trained to generate region proposals.They simoultaneously regress region bounds and bjectness scores at each location on a regular grid. RPNs are one type of fully convolutional networks. They take an image of any size as input and output a set of rectangular object proposals, each with an objectness score. ## See also * [RCNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen) * [Fast RCNN](http://www.shortscience.org/paper?bibtexKey=conf/iccv/Girshick15#joecohen) * [Faster RCNN](http://www.shortscience.org/paper?bibtexKey=conf/nips/RenHGS15#martinthoma) * [Mask RCNN](http://www.shortscience.org/paper?bibtexKey=journals/corr/HeGDG17) 
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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 backpropagation of HernandezLobato 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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TLDR; The authors use an attention mechanism in image caption generation, allowing the decoder RNN focus on specific parts of the image. In order find the correspondence between words and image patches, the RNN uses a lower convolutional layer as its input (before pooling). The authors propose both a "hard" attention (trained using sampling methods) and "soft" attention (trained endtoend) mechanism, and show qualitatively that the decoder focuses on sensible regions while generating text, adding an additional layer of interpretability to the model. The attentionbased models achieve stateofthe art on Flickr8k, Flickr30 and MS Coco. #### Key Points  To find image correspondence use lower convolutional layers to attend to.  Two attention mechanisms: Soft and hard. Depending on evaluation metric (BLEU vs. METERO) one or the other performs better.  Largest data set (MS COCO) takes 3 days to train on Titan Black GPU. Oxford VGG.  Soft attention is same as for seq2seq models.  Attention weights are visualized by upsampling and applying a Gaussian #### Notes/Questions  Would've liked to see an explanation of when/how soft vs. hard attention does better.  What is the computational overhead of using the attention mechanism? Is it significant? 
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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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Interacting with the environment comes sometimes at a high cost, for example in high stake scenarios like health care or teaching. Thus instead of learning online, we might want to learn from a fixed buffer $B$ of transitions, which is filled in advance from a behavior policy. The authors show that several so called offpolicy algorithms, like DQN and DDPG fail dramatically in this pure offpolicy setting. They attribute this to the extrapolation error, which occurs in the update of a value estimate $Q(s,a)$, where the target policy selects an unfamiliar action $\pi(s')$ such that $(s', \pi(s'))$ is unlikely or not present in $B$. Extrapolation error is caused by the mismatch between the true stateaction visitation distribution of the current policy and the stateaction distribution in $B$ due to:  stateaction pairs (s,a) missing in $B$, resulting in arbitrarily bad estimates of $Q_{\theta}(s, a)$ without sufficient data close to (s,a).  the finiteness of the batch of transition tuples $B$, leading to a biased estimate of the transition dynamics in the Bellman operator $T^{\pi}Q(s,a) \approx \mathbb{E}_{\boldsymbol{s' \sim B}}\left[r + \gamma Q(s', \pi(s')) \right]$  transitions are sampled uniformly from $B$, resulting in a loss weighted w.r.t the frequency of data in the batch: $\frac{1}{\vert B \vert} \sum_{\boldsymbol{(s, a, r, s') \sim B}} \Vert r + \gamma Q(s', \pi(s'))  Q(s, a)\Vert^2$ The proposed algorithm BatchConstrained deep Qlearning (BCQ) aims to choose actions that: 1. minimize distance of taken actions to actions in the batch 2. lead to states contained in the buffer 3. maximizes the value function, where 1. is prioritized over the other two goals to mitigate the extrapolation error. Their proposed algorithm (for continuous environments) consists informally of the following steps that are repeated at each time $t$: 1. update generator model of the state conditional marginal likelihood $P_B^G(a \vert s)$ 2. sample n actions form the generator model 3. perturb each of the sampled actions to lie in a range $\left[\Phi, \Phi \right]$ 4. act according to the argmax of respective Qvalues of perturbed actions 5. update value function The experiments considers Mujoco tasks with four scenarios of batch data creation:  1 million time steps from training a DDPG agent with exploration noise $\mathcal{N}(0,0.5)$ added to the action.This aims for a diverse set of states and actions.  1 million time steps from training a DDPG agent with an exploration noise $\mathcal{N}(0,0.1)$ added to the actions as behavior policy. The batchRL agent and the behavior DDPG are trained concurrently from the same buffer.  1 million transitions from rolling out a already trained DDPG agent  100k transitions from a behavior policy that acts with probability 0.3 randomly and follows otherwise an expert demonstration with added exploration noise $\mathcal{N}(0,0.3)$ I like the fourth choice of behavior policy the most as this captures high stake scenarios like education or medicine the closest, in which training data would be acquired by human experts that are by the nature of humans not optimal but significantly better than learning from scratch. The proposed BCQ algorithm is the only algorithm that is successful across all experiments. It matches or outperforms the behavior policy. Evaluation of the value estimates showcases unstable and diverging value estimates for all algorithms but BCQ that exhibits a stable value function. The paper outlines a very important issue that needs to be tackled in order to use reinforcement learning in real world applications. 
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This paper presents an approach to initialize a neural network from the parameters of a smaller and previously trained neural network. This is effectively done by increasing the size (in width and/or depth) of the previously trained neural network, in such of a way that the function represented by the network doesn't change (i.e. the output of the larger neural network is still the same). The motivation here is that initializing larger neural networks in this way allows to accelerate their training, since at initialization the neural network will already be quite good. In a nutshell, neural networks are made wider by adding several copies (selected randomly) of the same hidden units to the hidden layer, for each hidden layer. To ensure that the neural network output remains the same, each incoming connection weight must also be divided by the number of replicas that unit is connected to in the previous layer. If not training using dropout, it is also recommended to add some noise to this initialization, in order to break its initial symmetry (though this will actually break the property that the network's output is the same). As for making a deeper network, layers are added by initializing them to be the identity function. For ReLU units, this is achieved using an identity matrix as the connection weight matrix. For units based on sigmoid or tanh activations, unfortunately it isn't possible to add such identity layers. In their experiments on ImageNet, the authors show that this initialization allows them to train larger networks faster than if trained from random initialization. More importantly, they were able to outperform their previous validation set ImageNet accuracy by initializing a very large network from their best Inception network. 
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Shaham et al. provide an interpretation of adversarial training in the context of robust optimization. In particular, adversarial training is posed as minmax problem (similar to other related work, as I found): $\min_\theta \sum_i \max_{r \in U_i} J(\theta, x_i + r, y_i)$ where $U_i$ is called the uncertainty set corresponding to sample $x_i$ – in the context of adversarial examples, this might be an $\epsilon$ball around the sample quantifying the maximum perturbation allowed; $(x_i, y_i)$ are training samples, $\theta$ the parameters and $J$ the trianing objective. In practice, when the overall minimization problem is tackled using gradient descent, the inner maximization problem cannot be solved exactly (as this would be inefficient). Instead Shaham et al. Propose to alternatingly make single steps both for the minimization and the maximization problems – in the spirit of generative adversarial network training. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/). 
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#### Introduction * The paper explores the domain of conditional image generation by adopting and improving PixelCNN architecture. * [Link to the paper](https://arxiv.org/abs/1606.05328) #### Based on PixelRNN and PixelCNN * Models image pixel by pixel by decomposing the joint image distribution as a product of conditionals. * PixelRNN uses twodimensional LSTM while PixelCNN uses convolutional networks. * PixelRNN gives better results but PixelCNN is faster to train. #### Gated PixelCNN * PixelRNN outperforms PixelCNN due to the larger receptive field and because they contain multiplicative units, LSTM gates, which allow modelling more complex interactions. * To account for these, deeper models and gated activation units (equation 2 in the [paper](https://arxiv.org/abs/1606.05328)) can be used respectively. * Masked convolutions can lead to blind spots in the receptive fields. * These can be removed by combining 2 convolutional network stacks: * Horizontal stack  conditions on the current row. * Vertical stack  conditions on all rows above the current row. * Every layer in the horizontal stack takes as input the output of the previous layer as well as that of the vertical stack. * Residual connections are used in the horizontal stack and not in the vertical stack (as they did not seem to improve results in the initial settings). #### Conditional PixelCNN * Model conditional distribution of image, given the highlevel description of the image, represented using the latent vector h (equation 4 in the [paper](https://arxiv.org/abs/1606.05328)) * This conditioning does not depend on the location of the pixel in the image. * To consider the location as well, map h to spatial representation $s = m(h)$ (equation 5 in the the [paper](https://arxiv.org/abs/1606.05328)) #### PixelCNN AutoEncoders * Start with a traditional autoencoder architecture and replace the deconvolutional decoder with PixelCNN and train the network endtoend. #### Experiments * For unconditional modelling, Gated PixelCNN either outperforms PixelRNN or performs almost as good and takes much less time to train. * In the case of conditioning on ImageNet classes, the log likelihood measure did not improve a lot but the visual quality of the generated sampled was significantly improved. * Paper also included sample images generated by conditioning on human portraits and by training a PixelCNN autoencoder on ImageNet patches. 
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In this article, the authors provide a framework for training two translation models with large accessible monolingual corpus. In traditional methods, machine translation models always require large parallel corpus to train a good quality model, which is expensive to acquire. However, the massive monolingual data is not fully utilized. The monolingual corpus are typically used in pretraining the NMT decoder rnn and augmenting initial parallel corpus through selfgenerated translations. The authors embed machine translation task into a reinforcement learning framework, in which two agents act as two different native speakers respectively and know little about each other and then they learn to translate by trying to communicate with each other. **The two speakers**, `A` and `B`, obviously know well about their corresponding language respectively, this situation is easily simulated by two welltrained language models for `A` and `B`. Then, speaker `A` tries to tell a sentence $x$ to `B` by translating it into $y$ in `B`'s language. Since they don't know each other, `B` is uncertain about what `A` truly means by saying $y$. However, `B` is capable of evaluate the degree of sensibility of $y$ from his own understanding. Next, `B` informs `A` his sensibility evaluation score and tries to recover what `A` truly means in `A`'s language, i.e. $x'$. And similarly, `A` can also evaluate the degree of sensibility of $x'$ from his own understanding. In general, the very original idea that `A` tried to convey, is passed through a noisy channel to `B`, and then back to `A` through another noisy channel. The former noisy channel is a `AB` translation model and the latter a `BA` translation model in the framework. Think about how the first American learnt Chinese in history and I think it is intuitively similar to the principle in this work.
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This paper describes an architecture designed for generating class predictions based on a set of features in situations where you may only have a few examples per class, or, even where you see entirely new classes at test time. Some prior work has approached this problem in ridiculously complex fashion, up to and including training a network to predict the gradient outputs of a metanetwork that it thinks would best optimize loss, given a new class. The method of Prototypical Networks prides itself on being much simpler, and more intuitive, so I hope I’ll be able to convey that in this explanation. In order to think about this problem properly, it makes sense to take a few steps back, and think about some fundamental assumptions that underly machine learning. https://i.imgur.com/Q45w0QT.png One very basic one is that you need some notion of similarity between observations in your training set, and potential new observations in your test set, in order to properly generalize. To put it very simplistically, if a test example is very similar to examples of class A that we saw in training, we might predict it to be of class A at testing. But what does it *mean* for two observations to be similar to one another? If you’re using a method like K Nearest Neighbors, you calculate a point’s class identity based on the closest trainingset observations to it in Euclidean space, and you assume that nearness in that space corresponds to likelihood of two data points having come the same class. This is useful for the use case of having new classes show up after training, since, well, there isn’t really a training period: the strategy for KNN is just carrying your whole training set around, and, whenever a new test point comes along, calculating it’s closest neighbors among those trainingset points. If you see a new class in the wild, all you need to do is add the examples of that class to your group of training set points, and then after a few examples, if your assumptions hold, you’ll be able to predict that class by (hopefully) finding those two or three points as neighbors. But what if some dimensions of your feature space matter much more than others for differentiating between classes? In a simplistic example, you could have twenty features, but, unbeknownst to you, only one is actually useful for separating out your classes, and the other 19 are random. If you use the naive KNN assumption, you wouldn’t expect to perform well here, because you will have distances in these 19 meaningless directions spreading out your points, due to randomness, more than the meaningful dimension spread them out due to belonging to different classes. And what if you want to be able to learn nonlinear relationships between your features, which the composability of multilayer neural networks lends itself well to? In cases like those, the features you were handed may be a woefully suboptimal metric space in which to calculate a kind of similarity that corresponds to differences in class identity, so you’ll just have to strike out for the territories and create a metric space for yourself. That is, at a very high level, what this paper seeks to do: learn a transformation between input features and some vector space, such that distances in that vector space correspond as well as possible to probabilities of belonging to a given output class. You may notice me using “vector space” and “embedding” similarity; they are the same idea: the result of that learned transformation, which represents your input observations as dense vectors in some pdimensional space, where p is a chosen hyperparameter. What are the concrete learning steps this architecture goes through? 1. During each training episode, sample a subset of classes, and then divide those classes into training examples, and query examples 2. Using a set of weights that are being learned by the network, map the input features of each training example into a vector space. 3. Once all training examples are mapped into the space, calculate a “mean vector” for class A by averaging all of the embeddings of training examples that belong to class A. This is the “prototype” for class A, and once we have it, we can forget the values of the embedded examples that were averaged to create it. This is a nice update on the KNN approach, since the number of parameters we need to carry around to evaluate is only (numdimensions) * (numclasses), rather than (numdimensions) * (numtrainingexamples). 4. Then, for each query example, map it into the embedding space, and use a distance metric in that space to create a softmax over possible classes. (You can just think of a softmax as a network’s predicted probability, it’s a set of floats that add up to 1). 5. Then, you can calculate the (crossentropy) error between the true output and that softmax prediction vector in the same way as you would for any classification network 6. Add up the prediction loss for all the query examples, and then backpropogate through the network to update your weights The overall effect of this process is to incentivize your network to learn, not necessarily a good prediction function, but a good metric space. The idea is that, if the metric space is good enough, and the classes are conceptually similar to each other (i.e. car vs chair, as opposed to car vs themeaningoflife), a space that does well at causing similar observed classes to be close to one another will do the same for classes not seen during training. I admit to not being sufficiently familiar with the datasets used for testing to have a sense for how well this method compares to more fully supervised classification schemes; if anyone does, definitely let me know! But the paper claims to get state of the art results compared to other approaches in this domain of fewshot learning (matching networks, and the aforementioned metalearning). One interesting note is that the authors found that squared Euclidean distance, when applied within the embedded space, worked meaningfully better than cosine distance (which is a more standard way of measuring distances between vectors, since it measures only angle, rather than magnitude). They suspect that this is because Euclidean distance, but not cosine distance belongs to a category of divergence/distance metrics (called Bregman Divergences) that have a special set of properties such that the point closest on aggregate to all points in a cluster is the average of all those points. If you want to dive way deep into the minutia on this point, I found this blog post quite good: http://mark.reid.name/blog/meetthebregmandivergences.html
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