Learn faster with smarter data labeling
Findings on optimizing the labeling process with different active learning techniques
The vast amount of deep learning tools enables us to quickly build new apps with incredible performance, from computer vision classifying complex objects on photo to natural language understanding by extracting semantics from texts.
However, the main bottleneck for all these methods is the massive amount of data needed to train all these models — typically hundred thousands of training examples.
If you are starting to build image classifier from scratch, say, for detecting stale products on a lane, you’ll get stuck for weeks or months to collect and manually annotate all these photos.
Fortunately, there are a bunch of deep neural networks, that are already pre-trained on large image datasets with many classes, and ready to use for mitigating that cold start problem. The core idea of transfer learning is to leverage the outputs of these models, capturing high-level image semantics, as inputs to new classifiers that solve the target task.
It significantly reduces the amount of data needed to be annotated by human labelers, from hundred thousand to thousands of images.
However, even annotating thousands of examples could be expensive, especially if each annotation task relies on subject matter expert’s knowledge. Ideally, it would be perfect for labeling only a few hundreds of tasks and letting deep learning machinery do self-learning without any supervision.
This problem is also known as budgeted learning: we economize the amount of money to be spent on acquiring training dataset to build the model of required performance. Another problem is related to a concept drift — when the target task is changing over time (new products are coming to the detector’s line) and prediction performance degrades without human intervention.
So, besides what transfer learning offers, can we further reduce the amount of labeling work? Actually, the answer is yes, and there are a couple of techniques that exist. One of the most well studied is active learning. The principle is straightforward: only label what is useful for your current model. Formally, the active learning algorithm could be summarized in the following steps:
- Train initial model
- Pick one most useful sample (one that model is uncertain about, based on it’s predicted class probabilities)
- Label this sample and add it to a training set
- Retrain model on a new training set
- Repeat from step 2
This heuristic works pretty well with old-school machine learning models (e.g. linear classifiers like logistic regression, SVM, etc.), but empirical studies suggest that it becomes non effective in deep learning setting.
Another rarely mentioned drawback is that classical active learning requires retraining the model after each new labeled sample, which is a time-consuming process.
So, summarizing, to enable a quick training of a new classifier without any labeled data, with minimal human efforts, we have to deal with the following:
- Make use of the feature space produced by some high-quality pre-trained models
- Select only the most informative samples to be labelled by human annotators
- Quickly update our current machine learning state
The first step is very well studied in many research papers and reviews, and even large enterprise companies share nowadays their pre-trained neural networks, that could be very easily plugged into feature extraction pipeline in the manner vectorized_examples = pretrained_model(raw_examples). The ideal starting point is to look at TensorflowHub
Next steps are a tricky art of machine learning. Though intuition behind the informativeness of the sample is clear, it’s unclear how to calculate it accurately. Therefore there exist many different measures based on class probabilities p₁(x), p₂(x), …, pᵢ(x),…, sorted in descending order:
Which are all subjected to be maximized over seekable example set x ∈ X. Once we found such ambiguous example x, we hopefully see the model be much more confident in the neighborhood of x after retraining. But what if there are no items in the neighborhood of x?
Actually this example, despite its model uncertainty, doesn’t give us enough information for faster model convergence, so we have to exclude such outliers from our search space. This can be done by maximizing:
also called the informational density of an example. By maximizing i(x), we pick only those points that are densely surrounded by other points in our feature space.
Uncertainty measures coupled with informational density allow us to pick points which are excellent representatives and still gives new knowledge to the model.
But if there are two representative points where model is unconfident, how do you choose one? One way is to choose the most dissimilar example to those already picked. The last ingredient informative measure is diversity:
that promotes to pick as many diverse points as possible.
To combine these 3 measures in one scoring function, it is possible to use the product of theirs corresponding cumulative distribution functions (CDFs) (otherwise you should deal with numbers at the different scales or even negative numbers):
where each capital letter function denotes CDF, e.g. U(x) = P(u < u(x))
Getting results faster
Finally, we’ve picked the example and can label it (there are many data labeling tools available. The last step is to train the model. It can be a long wait of hours until the model finishes it’s retraining process and we can proceed with the next sample.
So what about if we do NOT train at all? Sounds crazy, but, if your feature space exists without any additional parameters to be learned, then you can rely on embeddings it produces and use simple metrics like euclidean and cosine distances.
This technique is well known as a k-Nearest Neighbor classifier: for any sample, assign unknown class from closest neighbor’s class (or from k closest neighbors).
Despite its simplicity, it is a very powerful method since:
- It enables “learning” the new model instantly — actually no learning is performed, the labeled example is just stored in some efficient data structure (unless you are using brute-force search)
- It produces a non-linear model
- It can be easily scaled and deployed without model retraining — simply add new vectors to your running model
- If simple distance like euclidean is not enough, new metric spaces could be learned offline (see an overview of some metric learning methods as well as few-shot learning
- It also offers some explainability: when classifying some unknown example, it is always possible to investigate what has influenced this prediction (since we have access to all training set, that is infeasible only with learned parameters)
Here is a visualisation how it looks in practice:
There are 2 classes, purples (p), and oranges (o). At each step, our selection method tries to do the best to get the most informative example and label it. As you can see, the model always tries to pick the next sample in between 1st and 2nd class points, mainly because maximizing uncertainty measure u(x) leads to minimizing
That leads to border exploration (around X=0) during ~30 first steps. Then our active selection methods try to dive into a dense cluster of points, thanks for informational density.
Moreover, it’s always aimed to explore different points due to diversity measure, that is way better: conventional active learning gets stuck on only one borderline and could neglect different point clusters.
Validation and Results
To validate this active sampling method, let’s run an experiment on the real dataset of fresh/rotten fruits images.
There are 3 types of fruits (apples, bananas, and oranges), each class is exposed by fresh and rotten pictures, so there are 6 classes in total. 90% of the dataset is retained for training purposes, and the rest is for testing. To mitigate statistical errors, K-fold cross-validation is applied with K=16, and test errors are averaged afterward.
We take the 1280-dimensional output of MobileNet-v2 neural network as embeddings for our feature space and adopt cosine distance for metric space. Here are some insights on how the embedded dataset looks like
You can see that there are some cloud clusters so that we can benefit here by using additional informativeness measures besides uncertainty.
The averaged model performance against the number of labeled examples is depicted here. As we can see, active sampling strategy needs almost 2x times less labelling tasks to reach 90% accuracy compared with the strategy, where points are randomly selected for labeling.
Another experiment was conducted on textual data, taken from Sentiment140 dataset. The task consists of classifying tweets into positive/negative sentiment.
For experimentation purposes, we are randomly sampling 20k points and do the same experiment as with fresh/rotten fruit images above. As a feature extractor, Universal Sentence Encoder is taken that produces 512-dimensional embeddings for each tweet.
Similarly, here is how the model performs over labeling steps comparing active & random selection strategies. Again, active selection supposed to converge much faster, though the absolute performance is lower (only 62% of accuracy and 70% is the best accuracy when you use the full dataset).
- When the labeling process is the bottleneck to build your next machine learning project, use active learning to minimize the number of labeling tasks
- Use pre-trained deep neural network’s outputs to convert your tasks from raw data (images, texts) to vectors (embeddings)
- Apply a combination of informativeness measures to pick next training samples, to decrease model uncertainty, promote representativeness and diversity
- Choose k-NN as your classifier when you want to do instant training and fast and transparent prediction
- Compare your active learning results on each step with uniform sampling strategy on holdout dataset to see how performance evolves over picking steps and how you can economize your labelling budget