The paper discusses and empirically investigates by empirical testing the effect of "catastrophic forgetting" (**CF**), i.e. the inability of a model to perform a task it was previously trained to perform if retrained to perform a second task. 

An illuminating example is what happens in ML systems with convex objectives: regardless of the initialization (i.e. of what was learnt by doing the first task), the training of the second task will always end in the global minimum, thus totally "forgetting" the first one. 

Neuroscientific evidence (and common sense) suggest that the outcome of the experiment is deeply influenced by the similarity of the tasks involved. Namely, if (i) the two tasks are *functionally identical but input is presented in a different format* or if (ii)  *tasks are similar* and the third case for (iii) *dissimilar tasks*. 

Relevant examples may be provided respectively by (i) performing the same image classification task starting from two different image representations as RGB or HSL, (ii) performing image classification tasks with semantically similar as classifying two similar animals and (iii) performing a text classification followed by image classification. 

The problem is investigated by an empirical study covering two methods of training ("SGD" and "dropout") combined with 4 activations functions (logistic sigmoid, RELU, LWTA, Maxout). A random search is carried out on these parameters. 

From a practitioner's point of view, it is interesting to note that dropout has been set to 0.5 in hidden units and 0.2 in the visible one since this is a reasonably well-known parameter. 

## Why the paper is important
It is apparently the first to provide a systematic empirical analysis of CF. Establishes a framework and baselines to face the problem.

## Key conclusions, takeaways and modelling remarks
* dropout helps in preventing CF
* dropout seems to increase the optimal model size with respect to the model without dropout 
* choice of activation function has a less consistent effect than dropout\no dropout choice
* dissimilar task experiment provides a  notable exception of then dissimilar task experiment
* the previous hypothesis that LWTA activation is particularly resistant to CF is rejected  (even if it performs best in the new task in the dissimilar task pair the behaviour is inconsistent)
* choice of activation function should always be cross-validated
* If computational resources are insufficient for cross-validation the combination dropout + maxout activation function is recommended.

The main contributions of [Distributed representations of words and phrases and their compositionality](http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf) by Mikolov et al. are several extension to their previously introduced skip-gram (and CBOW) model for word vector representations learned on large text corpora.

For a given word in a training text corpus, the skip-gram model tries to predict surrounding words. Thus, training the skip-gram model on a text corpus adapts vector representations of words in a way that  maximizes the probability for correct surrounding words. This leads to distributed vector representations that capture a large number of precise syntactic and semantic word relationships.

They propose a method to identify idiomatic phrases that are not compositions of individual words (like “Boston Globe”) in order to improve the vocabulary base. Also, an alternative training method to the hierarchical softmax, negative sampling, is introduced and analysed in detail. Further, they show that meaningful linear arithmetic operations can be performed on the trained vector representations, which makes precise analogical reasoning possible.

Proposes a two-stage approach for continual learning. An active learning phase and a consolidation phase. The active learning stage optimizes for a specific task that is then consolidated into the knowledge base network via Elastic Weight Consolidation (Kirkpatrick et al., 2016). The active learning phases uses a separate network than the knowledge base, but is not always trained from scratch - authors suggest a heuristic based on task-similarity. Improves EWC by deriving a new online method so parameters don’t increase linearly with the number of tasks.

Desiderata for a continual learning solution:

- A continual learning method should not suffer from catastrophic forgetting. That is, it should be able to perform reasonably well on previously learned tasks. 

- It should be able to learn new tasks while taking advantage of knowledge extracted from previous tasks, thus exhibiting positive forward transfer to achieve faster learning and/or better final performance. 

- It should be scalable, that is, the method should be trainable on a large number of tasks. 

- It should enable positive backward transfer as well, which means gaining improved performance on previous tasks after learning a new task which is similar or relevant. 

- Finally, it should be able to learn without requiring task labels, and ideally, it should even be applicable in the absence of clear task boundaries.

Experiments:

- Sequential learning of handwritten characters of 50 alphabets taken from the Omniglot dataset.
- Sequential learning of 6 games in the Atari suite (Bellemare et al., 2012) (“Space Invaders”, “Krull”, “Beamrider”, “Hero”, “Stargunner” and “Ms. Pac-man”).
- 8 navigation tasks in 3D environments inspired by experiments with Distral (Teh et al., 2017).

This paper talks about how to compare National Land Cover Database data which can be represented as histograms. The challenge is scaling the computations. The question this paper asks is if a semantically aware histogram comparison is worth the extra computation. It turns out that is does not appear worth it but interesting findings are discussed. 

![](http://i.imgur.com/5mKnkQw.png)

This paper models object detection as a regression problem for bounding
boxes and object class probabilities with a single pass through the CNN. The
main contribution is the idea of dividing the image into a 7x7 grid, and having
each cell predict a distribution over class labels as well as a bounding box
for the object whose center falls into it. It's much faster than R-CNN and
Fast R-CNN, as the additional step of extracting region proposals has been
removed.

## Strengths

- Works real-time. Base model runs at 45fps and a faster version goes up to
150fps, and they claim that it's more than twice as fast as other works on
real-time detection.

- End-to-end model; Localization and classification errors can be jointly
optimized.

- YOLO makes more localization errors and fewer background mistakes than
Fast R-CNN, so using YOLO to eliminate false background detections from
Fast R-CNN results in ~3% mAP gain (without much computational time as R-CNN
is much slower).

## Weaknesses / Notes

- Results fall short of state-of-the-art: 57.9% v/s 70.4% mAP (Faster R-CNN).

- Performs worse at detecting small objects, as at most one object per grid
cell can be detected.