This very new paper, is currently receiving quite a bit of attention by the [community](https://www.reddit.com/r/MachineLearning/comments/5qxoaz/r_170107875_wasserstein_gan/).

The paper describes a new training approach, which solves the two major practical problems with current GAN training:

1) The training process comes with a meaningful loss. This can be used as a (soft) performance metric and will help debugging, tune parameters and so on.

2) The training process does not suffer from all the instability problems. In particular the paper reduces mode collapse significantly.

On top of that, the paper comes with quite a bit mathematical theory, explaining why there approach works and other approachs have failed. This paper is a must read for anyone interested in GANs.

**Background:** The goal of this work is to indicate image features which are relevant to the prediction of a neural network and convey that information to the user by displaying a counterfactual image animation.

**The Latent Shift Method:** This method works on any pretrained encoder/decoder and classifier which is differentiable. No special considerations are needed during model training. With this approach they want the exact opposite of an adversarial attack but it is using the same idea. They want to perturb the input image so that the classifier reduces its prediction. If they just compute $\frac{\partial f}{\partial x}$ and move the pixels directly then they will get an imperceivable difference like an adversarial attack. Using a decoder they can regularize the transformation so it will only yield value images.

The encoder takes the input image and encodes it into a latent representation $z$. Then the decoder reconstructs the image and feeds this image into the classifier. The gradient is computed from the output of the classifier with respect to $z$. Subtracting the gradient from z and reconstructing the image generates a counterfactual.

https://i.imgur.com/iuZGUTH.gif

They found that if they change the prediction by -30% the images come out pretty good. So an iterative search along the vector defined by the gradient in the latent space until the prediction is reduced by 30%.

From this sequence a 2D image can be reconstructed which is similar to a traditional attribution map by taking the maximum pixel wise difference between every image and the unperturbed reconstruction.

https://i.imgur.com/V3PCgXZ.png

The results look great!

https://i.imgur.com/DBki84c.gif

https://i.imgur.com/kFfQNKD.gif

In order to validate if this approach can help spot false positive predictions, two radiologists to evaluate how confident they were in a models predictions. For each image, radiologists viewed the prediction in two ways, using traditional methods or the Latent Shift images. Traditional methods includes the image gradient, guided backprop, and integrated gradients. The Latent Shift Counterfactual includes the animation as well as the 2D version.

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

What they would like to see, that for true positives, the results are all 5 and for false positives they are all 1. 
What they observe however, is that many false positives still cause high confidence in the model predictions but not as much as the true positives. Between these two methods they find for true positives that the latent shift counterfactuals show a significant increase in confidence which is good.

> 0.15±0.95 confidence increase using the Latent Shift method  (p=0.01).

For false positives they find an increase in confidence but it is not significant. 

> 0.04±1.06 increase which is not significant (p=0.57)

**Conclusions:**
 - Latent Shift's ability to generate counterfactuals is pretty good!
 - Vanilla autoencoders are sufficient for some pathologies.
 - StyleGAN and higher quality models should improve performance.
 - IoU analysis may not be the best fit.
 - Explainable AI methods can have an impact on the user confidence in the model.

(Disclaimer: I am the author of this work)

Project Website: https://mlmed.org/gifsplanation/

This paper proposes a 3D human pose estimation in video method based on the dilated temporal convolutions applied on 2D keypoints (input to the network). 2D keypoints can be obtained using any person keypoint detector, but Mask R-CNN with ResNet-101 backbone, pre-trained on COCO and fine-tuned on 2D projections from Human3.6M, is used in the paper.
https://i.imgur.com/CdQONiN.png

The poses are presented as 2D keypoint coordinates in contrast to using heatmaps (i.e. Gaussian operation applied at the keypoint 2D location). Thus, 1D convolutions over the time series are applied, instead of 2D convolutions over heatmaps. The model is a fully convolutional architecture with residual connections that takes a sequence of 2D poses ( concatenated $(x,y)$ coordinates of the joints in each frame) as input and transforms them through temporal convolutions.
https://i.imgur.com/tCZvt6M.png
The `Slice` layer in the residual connection performs padding (or slicing) the sequence with replicas of boundary frames (to both left and right) to match the dimensions with the main block as zero-padding is not used in the convolution operations.

3D pose estimation is a difficult task particularly due to the limited data available online. Therefore, the authors propose semi-supervised approach of training the 2D->3D pose estimation by exploiting unlabeled video. Specifically, 2D keypoints are detected in the unlabeled video with any keypoint detector, then 3D keypoints are predicted from them and these 3D points are reprojected back to 2D (camera intrinsic parameters are required). This is idea similar to cycle consistency in the [CycleGAN](https://junyanz.github.io/CycleGAN/), for instance.
https://i.imgur.com/CBHxFOd.png
In the semi-supervised part (bottom part of the image above) training penalizes when the reprojected 2D keypoints are far from the original input. Weighted mean per-joint position error (WMPJPE) loss, weighted by the inverse of the depth to the object (since far objects should contribute less to the training than close ones) is used as the optimization goal.

The two networks (`supervised` above, `semi-supervised` below) have the same architecture but do not share any weights. They are jointly optimized where `semi-supervised` part serves as a regularizer. They communicate through the path aiming to make sure that the mean bone length of the above and below branches match.

The interesting tendency is observed from the MPJPE analysis with different amounts of supervised and unsupervised data available. Basically, the `semi-supervised` approach becomes more effective when less labeled data is available.
https://i.imgur.com/bHpVcSi.png

Additionally, the error is reduced when the ground truth keypoints are used. This means that a robust and accurate 2D keypoint detector is essential for the accurate 3D pose estimation in this setting.
https://i.imgur.com/rhhTDfo.png

**Dropout for layers** sums it up pretty well. The authors built on the idea of [deep residual networks](http://arxiv.org/abs/1512.03385) to use identity functions to skip layers. 

The main advantages:

* Training speed-ups by about 25%
* Huge networks without overfitting

## Evaluation

* [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html): 4.91% error ([SotA](https://martin-thoma.com/sota/#image-classification): 2.72 %) Training Time: ~15h
* [CIFAR-100](https://www.cs.toronto.edu/~kriz/cifar.html): 24.58% ([SotA](https://martin-thoma.com/sota/#image-classification): 17.18 %) Training time: < 16h
* [SVHN](http://ufldl.stanford.edu/housenumbers/):  1.75% ([SotA](https://martin-thoma.com/sota/#image-classification): 1.59 %) - trained for 50 epochs, begging with a LR of 0.1, divided by 10 after 30 epochs and 35. Training time: < 26h

## 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)