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Did you know that in most machine learning projects, about 80% of the effort goes into data preparation, including data annotation? This heavy focus on data annotation stems from its pivotal role in determining the ML model's effectiveness. Nowadays, anyone can find ML model architectures online, but the real difference and competitive advantage for companies comes from the data they use to train models. Decreasing the time spent on data is hard, as data quality directly translates to model performance.  For ML engineers and model builders, data quality is critical. On the other hand, saving time is a top priority for project managers – they'll need to have the ready-made model as fast as possible.

But imagine if you could have the cake and eat it too. SuperAnnotate is launching a new feature that will automate data annotation and make it way more efficient, stepping into an era where one annotator can potentially do the work of ten. This is excellent news for machine learning teams looking to get more done without compromising quality. This innovation isn't just about improving efficiency; it's about transforming the economics and speed of your machine-learning projects, ensuring top-notch data quality while drastically cutting down on time and cost.

One-shot learning: Replacing manual annotation

Traditionally, data annotation has revolved around two main methods: manual annotation and AI-assisted labeling. While they have been the mainstay for some time, the drawbacks, such as lack of time efficiency for manual annotation and inability to handle outliers for AI-assisted labeling, raised the need for a better alternative. The future is shifting towards a more efficient and advanced approach known as one-shot learning.

One-shot learning is the process of learning and generalizing from minimal data, where only a single instance, or 'shot' of data, is needed to identify similarities between objects. This innovative method contrasts conventional techniques, offering a glimpse into the future of data annotation, where efficiency and accuracy are a priority.

One-shot annotation in SuperAnnotate

Here's the take. With SuperAnnotate's one-shot annotation tool, you can annotate images in bulk, increasing the speed and accuracy of the annotation process.

The video demonstrates how the tool works.

Here’s the breakdown of the tool video:

  1. One-shot annotate: You initially have an unlabeled dataset of animals. Your goal is to find horses in the dataset and label them. So you click on the item containing the horse and then click "Annotate similar."
  2. Find similar: Our foundational model then finds objects containing similar information to the one you selected and returns the patches of images where similar horses were found.
  3. Assign label: At this point, you can approve the correct model suggestions and add those annotations to your dataset.

You can follow the same process with other objects: simply select the object and click "Annotate similar." The model will identify similar items and provide suggestions. You can then review and assign appropriate labels as needed. It's important to note that this is an iterative process. Once you've undergone the first one-shot learning phase and received the initial proposals, you then update this initial model and get the evaluation of each object type. The annotation algorithm will perform better on your data the more you annotate, further accelerating your productivity. Based on your assessment, the updated model will get better proposals of those objects for which the model fails. This process is repeated until the model performs according to your requirements and adequately handles all the edge cases, rare classes, and outliers.

Elevate your annotation game with just one click! Request a demo of SuperAnnotate's one-shot annotation tool and discover how you can save time and improve accuracy in your data labeling projects.

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What to expect next from SuperAnnotate

With the introduction of SuperAnnotate’s new one-shot tool, we expect that the machine learning sector will see notable efficiencies and accuracy improvements in data annotation. This tool radically speeds up the annotation process by allowing bulk annotation, a particularly beneficial feature for large-scale projects. Its advanced algorithm improves accuracy by precisely identifying and labeling similar objects within datasets.

The one-shot annotation tool also promises to boost productivity. Each cycle of annotation and feedback makes the tool more adept, reducing the time taken for subsequent annotations. This both speeds up project timelines and also translates into cost savings, as less manual effort is required over time.

In essence, SuperAnnotate's one-shot annotation tool represents a forward-thinking step in data annotation, poised to set new benchmarks in AI model training by taking efficiency and quality in ML to the next level.

How does one-shot learning work?

Let's now understand the machine learning techniques working under the hood of one-shot learning. It's a blend of unsupervised and semi-supervised learning and a repetitive model refinement process until the desired performance is achieved.

Step 1: Unsupervised learning

One-shot annotation starts with unsupervised learning – you're using only one shot to get the initial proposals. This means that after clicking on the object of interest, the model runs algorithms to find all similar objects and return them to you. It has no data for pre-training and uses only the selected object for finding and proposing similar objects.

Step 2: Semi-supervised learning

At this point, semi-supervised learning comes into play. Let’s make sure we know what its core function is. Semi-supervised learning sits between supervised learning (where all data is labeled) and unsupervised learning (where no data is labeled). It uses a small amount of labeled data to guide the learning process, along with a large amount of unlabeled data to build a model and learn the shape of data distribution.

semi supervised learning
Semi-supervised learning

We can see that in the graph above. In the very beginning, you have one white and one black class. Based on the “closeness” of the data points, the model labels the near objects. We can see that the model assumes that data closest to each other have the same label. Iteration over iteration, the decision boundary between the labels is updated.

Step 3: Closing the loop

Once you’ve undergone the first one-shot learning phase and received the initial proposals, you then update this initial model and get the evaluation of each object type. Your model updates and has a better understanding of the object characteristics and your evaluation. The updated model will get better proposals of those objects for which the model fails based on your evaluation. This process is repeated in a cycle until the model performs according to your requirements and adequately handles all the edge cases, rare classes, and outliers.

Traditional annotation methods

One-shot learning is an innovative approach in data annotation. It gained popularity due to the limitations that traditional methods like manual annotation or AI-assisted labeling had. Let's explore more about this.

Manual annotation and its limitations

Traditionally, the data annotation task has been predominantly in the hands of human experts, who go through datasets by hand, annotating each element with precision. This manual annotation is known for its detail-oriented nature and accuracy (not always the case), as it relies on human judgment and understanding to provide context to data. However, as the digital universe expands and the data volume skyrockets, annotating data manually becomes increasingly complex. This process is time-consuming, recourse-intensive, and prone to human error, which is inevitable. Such drawbacks made manual annotation increasingly impractical. As a result, manual annotation is dead at the time.

AI-assisted labeling and its limitations

This challenge led to the rise of AI-assisted labeling, an approach where AI automates the annotation process. Employing AI models for labeling data significantly reduced the human workload.

However, while this approach improves efficiency, it introduces complexities, especially in accurately capturing and labeling rare or unique data instances, which are often critical for comprehensive and effective AI training. Dealing with outliers in data is a significant problem in data annotation. You can better understand this idea by the graph of long-tail distribution.

long tailed distribution
Long-tailed distribution

In such cases, we have a class imbalance problem –  a decent representation for only some parts of the classes but an underrepresentation of others (on the right side of the graph). This results in serious danger in many applications. Overlooking rare or unusual instances leads to reduced model robustness and potential safety risks in critical applications like healthcare and autonomous driving. Such oversight can also limit the problem-solving abilities of AI. Addressing these challenges is crucial for developing comprehensive, fair, and safe AI systems.

Wrapping up

To wrap it up, we've explored the evolving landscape of data annotation in machine learning, sharing insights into the challenges we've encountered with manual annotation and the complexities that come with AI-assisted labeling. We've introduced the concept of one-shot learning and SuperAnnotate's tool, simplifying the labeling process and addressing concerns about underrepresented data. By blending unsupervised and semi-supervised learning, we've found that it streamlines annotation, boosts AI model robustness, and offers a more efficient and secure path for AI development. As we embrace the transformative power of one-shot learning, it promises to reshape how we train and propel AI models into the future. So, stay tuned for what SuperAnnotate has in store as it continues to offer the most cutting-edge solutions in machine learning and data annotation.

Armine Papikyan

Content Marketer/ AI enthusiast

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