This paper deals with an important problem where a deep classification system is made explainable. After the (continuing) success of Deep Networks, researchers are trying to open the blackbox and this work is one of the foremosts. The authors explored the strength of a deep learning method (vision-language model) to explain the performance of another deep learning model (image classification). The approach jointly predicts a class label and explains why it predicted so in natural language.

The paper starts with a very important differentiation between two basic schools of *explnation* systems - the *introspection* explanation system and the *justification* explanation system. The introspection system looks into the model to get an explanation (e.g., "This is a Western Grebe because filter 2 has a high activation..."). On the other hand, a justification system justifies the decision by producing sentence details on how visual evidence is compatible with the system output (e.g., "This is a Western Grebe because it has red eyes..."). The paper focuses on *justification* explanation system and proposes a novel one.

The authors argue that unlike a description of an image or a sentence defining a class (not necessarily in presence of an image), visual explanation, conditioned on an input image, provides much more of an explanatory text on why the image is classified as a certain category mentioning only image relevant features. The broad outline of the approach is given in Fig (2) of the paper.
https://i.imgur.com/tta2qDp.png
The first stage consists of a deep convolutional network for classification which generates a softmax distribution over the classes. As the task handles fine-grained bird species classification, it uses a compact bilinear feature representation known to work well for the fine-grained classification tasks. The second stage is a stacked LSTM which generates natural language sentences or explanations justifying the decision of the first stage. The first LSTM of the stack receives the previously generated word. The second LSTM receives the output of the first LSTM along with image features and predicted label distribution from the classification network. This LSTM produces the sequence of output words until an "end-of-sentence" token is generated. The intuition behind using predicted label distribution for explanation is that it would inform the explanation generation model which words and attributes are more likely to occur in the description.

Two kinds of losses are used for the second stage *i.e.*, the language model. The first one is termed as the *Relevance Loss* which is the typical sentence generation loss that is seen in literature. This is the sum of cross-entropy losses of the generated words with respect to the ground truth words. Its role is to optimize the alignment between generated and ground truth sentences. However, this loss is not very effective in producing sentences which include class discriminative information. class specificity is a global sentence property. This is illustrated with the following example - *whereas a sentence "This is an all black bird with a bright red eye" is class specific to a "Bronzed Cowbird", words and phrases in the sentence, such as "black" or "red eye" are less class discriminative on their own.* As a result, cross entropy loss on individual words turns out to be less effective in capturing the global sentence property of which class specifity is an example. The authors address this issue by proposing an addiitonal loss, termed as the *Discriminative Loss* which is based on a reinforcement learning paradigm. Before computing the loss, a sentence is sampled. The sentence is passed through a LSTM-based classification network whose task is to produce the ground truth category $C$ given only the sampled sentence. The reward for this operation is simply the probability of the ground truth category $C$ given only the sentence. The intuition is - for the model to produce an output with a large reward, the generated sentence must include enough information to classify the original image properly. The *Discriminative Loss* is the expectation of the negative of this reward and a wieghted linear combination of the two losses is optimized during training.

My experience in reinforcement learning is limited. However, I must say I did not quite get why is sampling of the sentences required (which called for the special algorithm for backpropagation). If the idea is to see whether a generated sentence can be used to get at the ground truth category, could the last internal state of one of the stacked LSTM not be used? It would have been better to get some more intution behind the sampling operation. Another thing which (is fairly obvious but still I felt) is missing is not mentioning the loss used in the fine grained classification network.

The experimentation is rigorous. The proposed method is compared with four different baseline and ablation models - description, definition, explanation-label, explanation-discriminative with different permutation and combinations of the presence of two types losses, class precition informations etc. Also the evaluation metrics measure different qualities of the generated exlanations, specifically image and class relevances. To measure image relevance METEOR/CIDEr scores of the generated sentences with the ground truth (image based) explanations are computed. On the other hand, to measure the class relevance, CIDEr scores with class definition (not necessarily based on the images from the dataset) sentences are computed. The proposed approach has continuously shown better performance than any of the baseline or ablation methods. I'd specifically mention about one experiment where the effect of class conditioning is studies (end of Sec 5.2). The finding is quite interesting as it shows that providing or not providing correct class information has drastic effect at the generated explanations. It is seen that giving incorrect class information makes the explanation model hallucinate colors or attributes which are not present in the image but are specific to the class. This raises the question whether it is worth giving the class information when the classifier is poor on the first hand? But, I think the answer lies in the observation that row 5 (with class prediction information) in table 1 is always better than row 4 (no class prediction information). Since, row 5 is better than row 4, this means the classifier is also reasonable and this in turn implies that end-to-end training can improve all the stages of a pipeline which ultimately improves the overall performance of the system too!

In summary, the paper is a very good first step to explain intelligent systems and should encourage a lot more effort in this direction.

## 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)

This paper combines two ideas. The first is stochastic gradient Langevin dynamics (SGLD), which is an efficient Bayesian learning method for larger datasets, allowing to efficiently sample from the posterior over the parameters of a model (e.g. a deep neural network). In short, SGLD is stochastic (minibatch) gradient descent, but where Gaussian noise is added to the gradients before each update. Each update thus results in a sample from the SGLD sampler. To make a prediction for a new data point, a number of previous parameter values are combined into an ensemble, which effectively corresponds to Monte Carlo estimate of the posterior predictive distribution of the model.

The second idea is distillation or dark knowledge, which in short is the idea of training a smaller model (student) in replicating the behavior and performance of a much larger model (teacher), by essentially training the student to match the outputs of the teacher. 

The observation made in this paper is that the step of creating an ensemble of several models (e.g. deep networks) can be expensive, especially if many samples are used and/or if each model is large. Thus, they propose to approximate the output of that ensemble by training a single network to predict to output of ensemble. Ultimately, this is done by having the student predict the output of a teacher corresponding to the model with the last parameter value sampled by SGLD.

Interestingly, this process can be operated in an online fashion, where one alternates between sampling from SGLD (i.e. performing a noisy SGD step on the teacher model) and performing a distillation update (i.e. updating the student model, given the current teacher model). The end result is a student model, whose outputs should be calibrated to the bayesian predictive distribution.

Problem
----------
Given an unconstrained image, estimate:
1. 3d pose of human skeleton 
2. 3d body mesh


Contributions
-----------
1. full body mesh extraction from image
2. improvement of state of the art


Datasets
-------------
1. Leeds Sports
2. HumanEva
3. Human3.6M


Approach
----------------
Consider the problem both bottom-up and top-down.
1. Bottom-up: DeepCut cnn model to fit joints 2d positions onto the image.
2. top-down: A skinned multi-person linear model (SMPL) is fitted and projected onto 2d joint positions and image.

This paper describes a class of algorithms for classification or regression in the on-line setting.  That is, the data is a bunch of pairs $(X_t,Y_t)$ (where X may be a vector), and these data items arrive in some order: the algorithm must predict each $\hat{Y}_t$ using only the $X_t$ and previously seen pairs. In the regression setting, each mis-prediction has a loss that is like $(Y_t - \hat{Y}_t)^2$, and in the classification setting $Y_t$ is always 0 or 1 and the loss is $| Y_t - \hat{Y}_t |$.

Roughly, the algorithm makes linear predictions using some internal weight vector $(\hat{y} = w  * X)$, and does a gradient-descent like weight update. However, it tries to keep the q-norm (q can be any number) of the weight vector "small", preventing the weights themselves from becoming too large. The algorithm is actually simple, and the weight update takes advantage of link functions, which the author defines.  The majority of the paper is focused on deriving loss bounds, showing that the loss incurred by this algorithm isn't much worse than that incurred by the best weight vector, chosen in hindsight.  Typical readers will be interested in the first few pages, as the latter part of the paper is mainly technical proofs.