Gilmer et al. study the existence of adversarial examples on a synthetic toy datasets consisting of two concentric spheres. The dataset is created by randomly sampling examples from two concentric spheres, one with radius $1$ and one with radius $R = 1.3$. While the authors argue that difference difficulties of the dataset can be created by varying $R$ and the dimensionality, they merely experiment with $R = 1.3$ and a dimensionality of $500$. The motivation to study this dataset comes form the idea that adversarial examples can easily be found by leaving the data manifold. Based on this simple dataset, the authors provide several theoretical insights – see the paper for details.

Beneath theoretical insights, Gilmer et al. slso discuss the so-called manifold attack, an attack using projected gradient descent which ensures that the adversarial examples stays on the data-manifold – moreover, it is ensured that the class does not change. Unfortunately (as I can tell), this idea of a manifold attack is not studied further – which is very unfortunate and renders the question while this concept was introduced in the first place.

One of the main take-aways is the suggestion that there is a trade-off between accuracy (i.e. the ability of the network to perform well) and the average distance to an adversarial example. Thus, the existence of adversarial examples might be related to the question why deep neural networks perform very well.

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

They improve an existing error correction system by re-ranking its predictions. The basic approach uses machine translation to perform error correction on learner texts – the incorrect text is essentially translated into correct text. Here, they include a ranking SVM to score and reorder the n-best lists from the translation model.

The reranking features include various internal scores from the translation model, the rank in the original ordering, language model probabilities trained on large corpora, language model scores based on only the n-best list, word-level translation probabilities, and sentence length features. They show improvement on two error correction datasets.

https://i.imgur.com/RxAE11a.png

Example output from the models.

This paper proposed a way to do classification using primitive concepts such as color, shape, texture, etc.

The framework is simple, they have two sub-models:

(1) the first one is a trained VL model such as CLIP,  ViLT, and ALBEF. The input of this step is the primitive concepts or let's say, attribute concepts and an image, then the output will be the scores for each concept.

(2) the second one is a linear model that uses the concepts and their scores to do classification. This model is trained in a supervised manner.

https://i.imgur.com/7WMmGyv.png

Authors propose a technique to adapt CNN trained on rgb images to leverage depth images at test time. This is done by implementing a mid-level fusion of RGB and depth CNNs. 

The approach is based on the fact that CNN are largely task and category agnostic and domain specific at lower levels, but the opposite at higher levels.

Question they try to answer: is there a way to use large amount of labeled RGB data, along with some RGB-D data, to train detectors that can use RGB-D data at test time to boost performance over the RGB detector, even for objects that don't have depth labeled data?

The resulting RGB-D detector is able to utilize the depth data provided at test time to improve detection, without ever being trained on any depth data for some categories (U in the paper).

They average the fc6 activations, after relu, and proceed to the fc7 layer.
Begin by training the RGB network (AlexNet architecture), with all categories.
Then copy weights to depth network, and train with the available data. Depth data is HHA encoded, which encodes the image geocentrically using three channels: horizontal disparity, height above ground and angle between the pixel's local surface normal and the inferred gravity direction.
After this, it is produced the final network. For layers before the merge point, use the RGB and depth weights directly. Then merge as explained before. For layers after merge point, use RGB weights.

The authors claim that it improves by 21% the performance of the RGB-only detector trained on this data, besides other interesting results.

The task is to predict feature norms – object properties, for example is_yellow and is_edible for the word banana. They experiment with adding in image recognition features, in addition to using distributional word vectors.

An input word is used to retrieve 10 images from Google, these are passed through an ImageNet classifier to get feature vectors, and then averaged to get a vector representation for that word. A supervised model (partial least-squares regression) is then trained to predict vectors of feature norms based on the input vectors (image-based, distributional, or a combination). Including the image information helps quite a bit, especially for detecting properties like colour and shape.

https://i.imgur.com/4TYKhvm.png

Examples of predicted feature norms using the visual features.