Mask RCNN takes off from where Faster RCNN left, with some augmentations aimed at bettering instance segmentation (which was out of scope for FRCNN). Instance segmentation was achieved remarkably well in *DeepMask* , *SharpMask* and later *Feature Pyramid Networks* (FPN).

Faster RCNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is most evident in how RoIPool , the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. Mask RCNN fixes that by introducing RoIAlign in place of RoIPool.

#### Methodology

Mask RCNN retains most of the architecture of Faster RCNN. It adds the a third branch for segmentation. The third branch takes the output from RoIAlign layer and predicts binary class masks for each class.

##### Major Changes and intutions

**Mask prediction**

Mask prediction segmentation predicts a binary mask for each RoI using fully convolution - and the stark difference being usage of *sigmoid* activation for predicting final mask instead of *softmax*, implies masks don't compete with each other. This *decouples* segmentation from classification. The class prediction branch is used for class prediction and for calculating loss, the mask of predicted loss is used calculating Lmask.

Also, they show that a single class agnostic mask prediction works almost as effective as separate mask for each class, thereby supporting their method of decoupling classification from segmentation

**RoIAlign**

RoIPool first quantizes a floating-number RoI to the discrete granularity of the feature map, this quantized RoI is then subdivided into spatial bins which are themselves quantized, and finally feature values covered by each bin are aggregated (usually by max pooling). Instead of  quantization of the RoI boundaries
or bin bilinear interpolation is used to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and aggregate the result (using max or average).

**Backbone architecture**

Faster RCNN uses a VGG like structure for extracting features from image, weights of which were shared among RPN and region detection layers. Herein, authors experiment with 2 backbone architectures - ResNet based VGG like in FRCNN and ResNet based [FPN](http://www.shortscience.org/paper?bibtexKey=journals/corr/LinDGHHB16) based. FPN uses convolution feature maps from previous layers and recombining them to produce pyramid of feature maps to be used for prediction instead of single-scale feature layer (final output of conv layer before connecting to fc layers was used in Faster RCNN) 

**Training Objective**

The training objective looks like this 
![](https://i.imgur.com/snUq73Q.png)

Lmask is the addition from Faster RCNN. The method to calculate was mentioned above

#### Observation

Mask RCNN performs significantly better than COCO instance segmentation winners *without any bells and whiskers*. Detailed results are available in the paper

- Presents a simple visualization method based on “filter normalization.”
- Observed that __the deeper networks become, neural loss landscapes become more chaotic__; causes a dramatic drop in generalization error, and ultimately to a lack of trainability.
- Observed that __skip connections promote flat minimizers and prevent the transition to chaotic behavior__; helps explain why skip connections are necessary for training extremely deep networks.
- Quantitatively measures non-convexity.
- Studies the visualization of SGD optimization trajectories.

The paper introduces a sequential variational auto-encoder that generates complex images iteratively. The authors also introduce a new spatial attention mechanism that allows the model to focus on small subsets of the image. This new approach for image generation produces images that can’t be distinguished from the training data.

#### What is DRAW:
The deep recurrent attention writer (DRAW) model has two differences with respect to other variational auto-encoders. First, the encoder and the decoder are recurrent networks. Second, it includes an attention mechanism that restricts the input region observed by the encoder and the output region observed by the decoder.

#### What do we gain?
The resulting images are greatly improved by allowing a conditional and sequential generation. In addition, the spatial attention mechanism can be used in other contexts to solve the “Where to look?” problem.

#### What follows?
A possible extension to this model would be to use a convolutional architecture in the encoder or the decoder. Although this might be less useful since we are already restricting the input of the network.

#### Like:
* As observed in the samples generated by the model, the attention mechanism works effectively by reconstructing images in a local way.
* The attention model is fully differentiable.

#### Dislike:
* I think a better exposition of the attention mechanism would improve this paper.

Tree-based ML models are becoming increasingly popular, but in the explanation space for these type of models is woefully lacking explanations on a local level. Local level explanations can give a clearer picture on specific use-cases and help pin point exact areas where the ML model maybe lacking in accuracy.

**Idea**: We need a local explanation system for trees, that is not based on simple decision path, but rather weighs each feature in comparison to every other feature to gain better insight on the model's inner workings.

**Solution**: This paper outlines a new methodology using SHAP relative values, to weigh pairs of features to get a better local explanation of a tree-based model. The paper also outlines how we can garner global level explanations from several local explanations, using the relative score for a large sample space. The paper also walks us through existing methodologies for local explanation, and why these are biased toward tree depth as opposed to actual feature importance.

The proposed explanation model titled TreeExplainer exposes methods to compute optimal local explanation, garner global understanding from local explanations, and capture feature interaction within a tree based model.

This method assigns Shapley interaction values to pairs of features essentially ranking the features so as to understand which features have a higher impact on overall outcomes, and analyze feature interaction.

## Introduction

Two distinct research paradigms have studied how prior tasks or experiences can be used by an agent to inform future learning.

* Meta Learning:  past experience is used to acquire a prior over model parameters or a learning procedure, and typically studies a setting where a set of meta-training tasks are made available together upfront
* Online learning : a sequential setting where tasks are revealed one after another, but aims to attain zero-shot generalization without any task-specific adaptation.

We argue that neither setting is ideal for studying continual lifelong learning. Meta-learning deals with learning to learn, but neglects the sequential and non-stationary aspects of the problem. Online learning offers an appealing theoretical framework, but does not generally consider how past experience can accelerate adaptation to a new task.

## Online Learning

Online learning focuses on regret minimization. Most standard notion of regret is to compare to the cumulative loss of the best fixed model in hindsight:
https://i.imgur.com/pbZG4kK.png
One way minimize regret is with Follow the Leader (FTL):
https://i.imgur.com/NCs73vG.png

## Online Meta-learning Setting:

let $U_t$ be the update procedure for task $t$
e.g. in MAML:
https://i.imgur.com/Q4I4HkD.png


The overall protocol for the setting is as follows:
1. At round t, the agent chooses a model defined by $w_t$
2. The world simultaneously chooses task defined by $f_t$ 
3. The agent obtains access to the update procedure $U_t$, and uses it to update parameters as $\tilde w_t = U_t(w_t)$
4. The agent incurs loss $f_t(\tilde w_t )$. Advance to round t + 1.

the goal for the agent is to minimize regrets over rounds.
Achieving sublinear regrets means you're improving and converging to upper bound (joint training on all tasks)


## Algorithm and Analysis:

Follow the meta-leader (FTML): 
https://i.imgur.com/qWb9g8Q.png

FTML’s regret is sublinear (under some assumption)