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 fixed-size representation. This CNN is pretrained on pixel-to-pixel tasks such as RGB reconstruction, semantic segmentation, and depth estimation.

2. **Language**. Questions are encoded with 2-layer LSTMs with 128-d 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 image-question similarity of the last 5 frames via a dot product between image features (passed through an fc-layer to align with question features) and question encoding. This similarity is converted to attention weights via a softmax, and the attention-weighted 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=gVj-TeIJfrk) 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.

Liu et al. propose randomizing neural networks, implicitly learning an ensemble of models, to defend against adversarial attacks. In particular, they introduce Gaussian noise layers before regular convolutional layers. The noise can be seen as additional parameter of the model. During training, noise is randomly added. During testing, the model is evaluated on a single testing input using multiple random noise vectors; this essentially corresponds to an ensemble of different models (parameterized by the different noise vectors).

Mathemtically, the authors provide two interesting interpretations. First, they argue that training essentially minimizes an upper bound of the (noisy) inference loss. Second, they show that their approach is equivalent to Lipschitz regularization [1].

[1] M. Hein, M. Andriushchenko. Formal guarantees on the robustness of a classifier against adversarial manipulation. ArXiv:1705.08475, 2017.

Also view this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

This paper is about interactive Visual Question Answering (VQA) setting in which agents must ask questions about images to learn. This closely mimics how people learn from each other using natural language and has a strong potential to learn much faster with fewer data. It is referred as learning by asking (LBA) through the paper. The approach is composed of three models:
http://imisra.github.io/projects/lba/approach_HQ.jpeg

1. **Question proposal module** is responsible for generating _important_ questions about the image. It is a combination of 2 models:
    - **Question generator** model produces a question. It is LSTM that takes image features and question type (random choice from available options) as input and outputs a question. 
    - **Question relevance** model that selects questions relevant to the image. It is a stacked attention architecture network (shown below) that takes in generated question and image features and filters out irrelevant to the image questions.  https://i.imgur.com/awPcvYz.png

2. **VQA module** learns to predict answer given the image features and question. It is implemented as stacked attention architecture shown above.

3. **Question selection module** selects the most informative question to ask. It takes current state of VQA module and its output to calculate expected accuracy improvement (details are in the paper) to measure how fast the VQA module has a potential to improve for each answer. The single question selection (i.e. best question for VQA to improve the fastest) strategy is based on epsilon-greedy policy.

This method (i.e. LBA) is shown to be about 50% more data efficient than naive VQA method. As an interesting future direction of this work, the authors propose to use real-world images and include a human in the training as an answer provider.

**TL;DR**: You can increase batch size in advanced phases of training without hurting accuracy and gaining some speedup. You should multiply the learning rate by the same value you multiplied batch size.

**Long version**: Authors propose to increase batch size gradually, starting with a  small  batch  size $r$, and then progressively increase the batch size while adapting the learning rate $\alpha$ so that the ratio $\alpha/r$ remains constant (without taking in account scheduled LR reduce). In this paper, they double the batch size by schedule. At the same time, learning rate is decayed and then multiplied by 2 to compensate batch size increase: if in baseline lr is multiplied by $0.375$, it is multiplied by $0.75$ now.

The experiments on CIFAR-100 dataset show that the gradual increase of batch size allows to converge to the same values as constant small batch size. However, bigger batches allow faster training, providing $\times 1.5$ speedup on AlexNet, and around $\times 1.2$ speedup on ResNet and VGG, for both forward and backward passes on single GPU.

On multiple GPUs the approach allows to further increase batchsize. On fortunate setups authors manage to get up to $\times 1.6$ speedup compared to constant batch size equal to initial value, while the error is almost unchanged. For bigger batch sizes lr warmup is used.

For ImageNet, same behavior is shown for accuracy: gradual increase of batch size converges to same values as setup with initial batch size. Since authors haven't access to a system capable of processing large batches on ImageNet, no performance results are reported.

Engstrom et al. demonstrate that spatial transformations such as translations and rotations can be used to generate adversarial examples. Personally, however, I think that the paper does not address the question where adversarial perturbations “end” and generalization issues “start”. For larger translations and rotations, the problem is clearly a problem of generalization. Small ones could also be interpreted as adversarial perturbations – especially when they are computed under the intention to fool the network. Still, the distinction is not clear ...

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).