This recent paper, a collaboration involving some of the authors of MAML, proposes an intriguing application of techniques developed in the field of meta learning to the problem of unsupervised learning - specifically, the problem of developing representations without labeled data, which can then be used to learn quickly from a small amount of labeled data. As a reminder, the idea behind meta learning is that you train models on multiple different tasks, using only a small amount of data from each task, and update the model based on the test set performance of the model. 

The conceptual advance proposed by this paper is to adopt the broad strokes of the meta learning framework, but apply it to unsupervised data, i.e. data with no pre-defined supervised tasks. The goal of such a project is, as so often is the case with unsupervised learning, to learn representations, specifically, representations we believe might be useful over a whole distribution of supervised tasks. However, to apply traditional meta learning techniques, we need that aforementioned distribution of tasks, and we’ve defined our problem as being over unsupervised data.  How exactly are we supposed to construct the former out of the latter? This may seem a little circular, or strange, or definitionally impossible: how can we generate supervised tasks without supervised labels? 

https://i.imgur.com/YaU1y1k.png

The artificial tasks created by this paper are rooted in mechanically straightforward operations, but conceptually interesting ones all the same: it uses an off the shelf unsupervised learning algorithm to generate a fixed-width vector embedding of your input data (say, images), and then generates multiple different clusterings of the embedded data, and then uses those cluster IDs as labels in a faux-supervised task. It manages to get multiple different tasks, rather than just one - remember, the premise of meta learning is in models learned over multiple tasks - by randomly up and down-scaling dimensions of the embedding before clustering is applied. Different scalings of dimensions means different points close to one another, which means the partition of the dataset into different clusters. 

With this distribution of “supervised” tasks in hand, the paper simply applies previously proposed meta learning techniques - like MAML, which learns a model which can be quickly fine tuned on a new task, or prototypical networks, which learn an embedding space in which observations from the same class, across many possible class definitions are close to one another.

https://i.imgur.com/BRcg6n7.png

An interesting note from the evaluation is that this method - which is somewhat amusingly dubbed “CACTUs” - performs best relative to alternative baselines in cases where the true underlying class distribution on which the model is meta-trained is the most different from the underlying class distribution on which the model is tested. Intuitively, this makes reasonable sense: meta learning is designed to trade off knowledge of any given specific task against the flexibility to be performant on a new class division, and so it gets the most value from trade off where a genuinely dissimilar class split is seen during testing. 

One other quick thing I’d like to note is the set of implicit assumptions this model builds on, in the way it creates its unsupervised tasks. First, it leverages the smoothness assumptions of classes - that is, it assumes that the kinds of classes we might want our model to eventually perform on are close together, in some idealized conceptual space. While not a perfect assumption (there’s a reason we don’t use KNN over embeddings for all of our ML tasks) it does have a general reasonableness behind it, since rarely are the kinds of classes very conceptually heterogenous. Second, it assumes that a truly unsupervised learning method can learn a representation that, despite being itself sub-optimal as a basis for supervised tasks, is a well-enough designed feature space for the general heuristic of “nearby things are likely of the same class” to at least approximately hold. I find this set of assumptions interesting because they are so simplifying that it’s a bit of a surprise that they actually work: even if the “classes” we meta-train our model on are defined with simple Euclidean rules, optimizing to be able to perform that separation using little data does indeed seem to transfer to the general problem of “separating real world, messier-in-embedding-space classes using little data”.  

The paper designs some basic tests to compare saliency methods. It founds that some of the most popular methods are independent of model parameters and the data, meaning they are effectively useless.

## Methods compared

The paper compare the following methods: gradient explanation, gradient x input, integrated gradients, guided backprop, guided GradCam and SmoothGrad. They provide a refresher on those methods in the appendix.

All those methods can be put in the same framework. They require a classification model and an input (typically an image). The output of the method is an *explanation map* of the shape of the input where a higher value for a feature implies greater relevance in the decision of the model. 

## Metrics of comparison

The authors argue that visual inspection of the saliency maps can be misleading. They propose to compute the Spearman rank correlation, the structural similarity index (SSMI) and the Pearson correlation of the histogram of gradients. The authors point out that those metrics capture various notions of similarity, but it is an active area of research and those metrics are imperfect.

## First test: model parameters randomization

A saliency method must be dependent of model parameters, otherwise it cannot help us understand a model. In this test, the authors randomize the model parameters, layer per layer, starting from the top.

Surprisingly, methods such as guided backprop and guided gradcam are completely insensitive to model parameters, as illustrated on this Inception v3 trained on ImageNet:

![image](https://user-images.githubusercontent.com/8659132/61403152-b10b8000-a8a2-11e9-9f6a-cf1ed6a876cc.png)

Integrated gradients looks also dubious as the bird is still visible with a mostly fully randomized model, but the quantitative metrics reveal the difference is actually big between the two models.

## Second test: data randomization

It is well-known that randomly shuffling the labels of a dataset does not prevent a neural network from getting a high accuracy on the training set, though it does prevent generalization. The model is able to learn by either memorizing the data or finding spurious patterns. As a result, saliency maps obtained from such a network should have no clearly interpretable signal.

Here is the result for a ConvNet trained on MNIST and a shuffled MNIST:

![image](https://user-images.githubusercontent.com/8659132/61406757-7efe1c00-a8aa-11e9-9826-a859a373cb4f.png)

The results are very damning for most methods. Only gradients and GradCam are very different between both models, as confirmed by the low correlation.

## Discussion

- Even though some methods do no depend on model parameters and data, they might still depend on the architecture of the models, which could be of some use in some contexts.
- Methods that multiply the input with the gradient are dominated by the input.
- Complex saliency methods are just fancy edge detectors.
- Only gradient, smooth gradient and GradCam survives the sanity checks.

# Comments

- Why is their GradCam maps so ugly? They don't look like usual GradCam maps at all.
- Their tests are simple enough that it's hard to defend a method that doesn't pass them.
- The methods that are left are not very good either. They give fuzzy maps that are difficult to interpret.
- In the case of integrated gradients (IG), I'm not convinced this is sufficient to discard the method. IG requires a "baseline input" that represents the absence of features. In the case of images, people usually just set the image to 0, which is not at all the absence of a feature. The authors also use the "set the image to 0" strategy, and I'd say their tests are damning for this strategy, not for IG in general. I'd expect an estimation of the baseline such as done in [this paper](https://arxiv.org/abs/1702.04595) would be a fairer evaluation of IG.

Code: [GitHub](https://github.com/adebayoj/sanity_checks_saliency) (not available as of 17/07/19)

This paper describes an architecture designed for generating class predictions based on a set of features in situations where you may only have a few examples per class, or, even where you see entirely new classes at test time. Some prior work has approached this problem in ridiculously complex fashion, up to and including training a network to predict the gradient outputs of a meta-network that it thinks would best optimize loss, given a new class. The method of Prototypical Networks prides itself on being much simpler, and more intuitive, so I hope I’ll be able to convey that in this explanation.  In order to think about this problem properly, it makes sense to take a few steps back, and think about some fundamental assumptions that underly machine learning. 
https://i.imgur.com/Q45w0QT.png

One very basic one is that you need some notion of similarity between observations in your training set, and potential new observations in your test set, in order to properly generalize.  To put it very simplistically, if a test example is very similar to examples of class A that we saw in training, we might predict it to be of class A at testing. But what does it *mean* for two observations to be similar to one another? If you’re using a method like K Nearest Neighbors, you calculate a point’s class identity based on the closest training-set observations to it in Euclidean space, and you assume that nearness in that space corresponds to likelihood of two data points having come the same class. This is useful for the use case of having new classes show up after training, since, well, there isn’t really a training period: the strategy for KNN is just carrying your whole training set around, and, whenever a new test point comes along, calculating it’s closest neighbors among those training-set points. If you see a new class in the wild, all you need to do is add the examples of that class to your group of training set points, and then after a few examples, if your assumptions hold, you’ll be able to predict that class by (hopefully) finding those two or three points as neighbors. But what if some dimensions of your feature space matter much more than others for differentiating between classes? In a simplistic example, you could have twenty features, but, unbeknownst to you, only one is actually useful for separating out your classes, and the other 19 are random. If you use the naive KNN assumption, you wouldn’t expect to perform well here, because you will have distances in these 19 meaningless directions spreading out your points, due to randomness, more than the meaningful dimension spread them out due to belonging to different classes. And what if you want to be able to learn non-linear relationships between your features, which the composability of multi-layer neural networks lends itself well to? In cases like those, the features you were handed may be a woefully suboptimal metric space in which to calculate a kind of similarity that corresponds to differences in class identity, so you’ll just have to strike out for the territories and create a metric space for yourself. 

That is, at a very high level, what this paper seeks to do: learn a transformation between input features and some vector space, such that distances in that vector space correspond as well as possible to probabilities of belonging to a given output class. You may notice me using “vector space” and “embedding” similarity; they are the same idea: the result of that learned transformation, which represents your input observations as dense vectors in some p-dimensional space, where p is a chosen hyperparameter. 

What are the concrete learning steps this architecture goes through?
 
1. During each training episode, sample a subset of classes, and then divide those classes into training examples, and query examples 
2. Using a set of weights that are being learned by the network, map the input features of each training example into a vector space. 
3. Once all training examples are mapped into the space, calculate a “mean vector” for class A by averaging all of the embeddings of training examples that belong to class A. This is the “prototype” for class A, and once we have it, we can forget the values of the embedded examples that were averaged to create it. This is a nice update on the KNN approach, since the number of parameters we need to carry around to evaluate is only (num-dimensions) * (num-classes), rather than (num-dimensions) * (num-training-examples). 
4. Then, for each query example, map it into the embedding space, and use a distance metric in that space to create a softmax over possible classes. (You can just think of a softmax as a network’s predicted probability, it’s a set of floats that add up to 1). 
5. Then, you can calculate the (cross-entropy) error between the true output and that softmax prediction vector in the same way as you would for any classification network 
6. Add up the prediction loss for all the query examples, and then backpropogate through the network to update your weights 

The overall effect of this process is to incentivize your network to learn, not necessarily a good prediction function, but a good metric space. The idea is that, if the metric space is good enough, and the classes are conceptually similar to each other (i.e. car vs chair, as opposed to car vs the-meaning-of-life), a space that does well at causing similar observed classes to be close to one another will do the same for classes not seen during training. 

I admit to not being sufficiently familiar with the datasets used for testing to have a sense for how well this method compares to more fully supervised classification schemes; if anyone does, definitely let me know! But the paper claims to get state of the art results compared to other approaches in this domain of few-shot learning (matching networks, and the aforementioned meta-learning). 

One interesting note is that the authors found that squared Euclidean distance, when applied within the embedded space, worked meaningfully better than cosine distance (which is a more standard way of measuring distances between vectors, since it measures only angle, rather than magnitude). They suspect that this is because Euclidean distance, but not cosine distance belongs to a category of divergence/distance metrics (called Bregman Divergences) that have a special set of properties such that the point closest on aggregate to all points in a cluster is the average of all those points. If you want to dive way deep into the minutia on this point, I found this blog post quite good: http://mark.reid.name/blog/meet-the-bregman-divergences.html

Domain translation - for example, mapping from a summer to a winter scene, or from a photorealistic image to an object segmentation map - is often performed by GANs through something called cycle consistency loss. This model works by having, for each domain, a generator to map domain A into domain B, and a discriminator to differentiate between real images from domain B, and those that were constructed through the cross-domain generator. With a given image in domain A, training happens by using the A→B generator to map it into domain B, and then then B→ A generator to map it back the original domain. These generators are then trained using two losses: one based on the B-domain discriminator, to push the generated image to look like it belongs from that domain, and another based on the L2 loss between the original domain A image, and the image you get on the other end when you translate it into B and back again. 

This paper addresses an effect (identified originally in an earlier paper) where in domains with a many to one mapping between domains (for example, mapping a realistic scene into a domain segmentation map, where information is inherently lost by translating pixels to object outlines), the cycle loss incentivizes the model to operate in a strange, steganographic way, where it saves information about the that would otherwise be lost in the form of low-amplitude random noise in the translated image. This low-amplitude information can't be isolated, but can be detected in a few ways. First, we can simply examine images and notice that information that could not have been captured in the lower-information domain is being perfectly reconstructed. Second, if you add noise to the translation in the lower-information domain, in such a way as to not perceptibly change the translation to human eyes, this can cause the predicted image off of that translation to deteriorate considerably, suggesting that the model was using information that could be modified by such small additions of noise to do its reconstruction. 

https://i.imgur.com/08i1j0J.png

The authors of this paper ask whether it's possible to train models that don't perform this steganographic information-storing (which they call "self adversarial examples"). A typical approach to such a problem would be to train generators to perform translations with and without the steganographic information, but even though we can prove the existence of the information, we can't isolate it in a way that would allow us to remove it, and thus create these kinds of training pairs. The two tactics the paper uses are: 

1) Simply training the generators to be able to translate a domain-mapped image with noise as well as one without noise, in the hope that this would train it not use information that can be interfered with by the application of such noise. 

2) In addition to a L2 cycle loss, adding a discriminator to differentiate between the back-translated image and the original one. I believe the idea here is that if both of the encoders are adding in noise as a kind of secret signal, this would be a way for the discriminator to distinguish between the original and reconstructed image, and would thus be penalized.  

They find that both of these methods reduce the use of steganographic information, as determined both by sensitivity to noise (where less sensitivity of reconstruction to noise means less use of coded information) and reconstruction honesty (which constrains accuracy of reconstruction in many to one domains to be no greater than the prediction that a supervised predictor could make given the image from the compressed domain

This paper is concerned with the problem of predicting a sequence at the output, e.g. using an RNN. It aims at addressing the issue it refers to as exposure bias, which here refers to the fact that while at training time the RNN producing the output sequence is being fed the ground truth previous tokens (words) when producing the next token (something sometimes referred to as teacher forcing, which really is just maximum likelihood), at test time this RNN makes predictions using recursive generation, i.e. it is instead recursively fed by its own predictions (which might be erroneous). 

Moreover, it also proposes a training procedure that can take into account a rich performance measure that can't easily be optimized directly, such as the BLEU score for text outputs. 

The key observation is that the REINFORCE algorithm could be used to optimize the expectation of such arbitrarily complicated performance measures, for outputs produced by (stochastic) recursive generation. However, REINFORCE is a notoriously unstable training algorithm, which can often work terribly (in fact, the authors mention that they have tried using REINFORCE only, without success). Thus, they instead propose to gradually go from training according to maximum likelihood / teacher forcing to training using the REINFORCE algorithm on the expected performance measure.

The proposed procedure, dubbed MIXER (Mixed Incremental Cross-Entropy Reinforce), goes as follows:
1. Train model to optimize the likelihood of the target sequence, i.e. minimize the per time-step cross-entropy loss.
2. Then, for a target sequence of size T, optimize the cross-entropy for the T-Δ first time steps of the sequence and use Reinforce to get a gradient on the expected loss (e.g. negative BLEU) for the recursive generation of the rest of the Δ time steps.
3. Increase Δ and go back to 2., until Δ is equal to T.

Experiments on 3 text benchmarks (summarization, machine translation and image captioning) show that this approach yields models that produces much better outputs when not using beam search (i.e. using greedy recursive generation) to generate an output sequence, compared to other alternatives such as regular maximum likelihood and Data as Demonstrator (DaD). DaD is similar to the scheduled sampling method of Bengio et al. (see my note: \cite{conf/nips/BengioVJS15}), in that at training time, some of the previous tokens fed to the model are predicted tokens instead of ground truths. When using beam search, MIXER is only outperformed by DaD on the machine translation task.