Developing AI models is inherently a reiterative process, requiring consistent review and correction to the training data that enable models to make accurate predictions. Two different approaches aim to make that cyclical corrective process easier. Learn how interaction-assisted and model-based auto-labeling techniques help produce a groundbreaking degree of prediction accuracy, and which method wins out when they’re compared side by side.
The greatest advantage of human-in-the-loop (HITL) labeling is that it combines the best of what a human and a machine can offer to optimize machine learning development. Humans apply expertise and oversee the entire annotation workflow, while machines enable many labor-intensive processes to be automated and in turn, simplified. In this article, we’ll be comparatively exploring the greater role machines are playing in optimizing the annotation workflow, from interaction-assisted (or AI-assisted capabilities) to a model-based approach that takes over the entire process from start to finish.
Auto-Labeling Strategies for Computer Vision
Labeling images is a tedious and time-consuming task for many object detection efforts or projects, often requiring individual labelers to annotate each image by hand. These fully-manual methods are understandably inefficient and unsustainable considering the present-day needs to develop more complex machine learning models ever.
With ML model competence directly reliant on the accuracy of the data annotations they were trained on, it's only to be expected that the updated approaches to these manual methods prioritize saving annotators time and enhancing the level of precision of their annotations or labels.
As a result, following the idea of supplying solutions for an industry demand, a variety of alternative practices have gained prominence. Among the most promising of them are interaction-assisted (also referred to as AI-assisted labeling) and ML-based labeling techniques.
In this article, we'll be diving into the specific advantages of each group of techniques and zero in on how the ML model-based labeling approach ultimately provides the most benefit to streamlining the data labeling workflow.
Benefits of Interaction vs. Model-Based Labeling
Interactive-assisted auto labeling is a method meant to simplify data labeling tasks through the use of AI. Under the scope and context of supervised learning projects, the interactive approach automates specific labeling assignments, such as adding target attributes (or labels) for object detection and labeling tools for the standard annotation needs like bounding boxes, polygon, and segmentation.
Rather than the teeth-gritting and frustrating act of outlining each and every object within an image, labelers have the leisure of simple point and click AI-assisted annotation; therefore cutting down on the time they spend labeling and also ensuring that each annotation is applied as precisely as possible with the help of an algorithm that results in tight and ideally accurate annotations.
On the other hand, ML-based auto labeling is a similar but distinct method, similar enough in purpose - to make labeling training data a speedier and more reliably efficient affair; but different in the way it takes that concept to the next level. What interaction-assisted labeling has achieved in working alongside a human labeler to produce annotations as easily as with the click of a mouse button - ML-based labeling takes to the next level.
By automating those functions entirely and entrusting the data labeling process to an ML model. Doing so, boosts the benefits of accelerating annotations, especially when compared to the original and long-standing approach of manually labeling data.
A Tandem Approach
In order to develop cutting-edge models, equally inventive approaches should be adopted. While AI-assisted and model-based auto-labeling are two such approaches that enable data annotators to produce quality datasets capable of powering those large-scale projects, they're best utilized harmoniously and according to each project's requirements and application.
For instance, it might be more sensible to use AI-assisted methods with more common object-related tasks that solely require bounding boxes or basic segmentation. If there's more complex data that requires labeling and a considerable amount of it, bringing in a more efficient and time-saving approach like model-based labeling would make a greater difference.
There's also the middle ground option of using the methods in tandem and based on the nature of the data or different groupings that deal with varying content. From imagery featuring pedestrians at an intersection for self-driving applications to images of the correct terms for diseases that a medical diagnostic tool will store and distinguish between as needed in a real environment setting.
Challenges to Model-Based Labeling
Although, as stated in the preceding section, both interactive and model-based labeling offer a number of advantages to improving the data labeling workflow and overall efforts, no approach or method can be entirely without its challenges or risk of being improperly implemented in order to deliver the intended results; not unlike the care and attention-to-detail most commonly dedicated to the later, model training and iterative-specific phases of the ML cycle, in fact.
Beyond the importance of mindful implementation of these methods, it's highly advised and reminded that automation still requires some vetting or periodic monitoring to ensure they continue to perform properly over time.
That means double-checking ML-generated labels to ensure their accuracy and quality, particularly before they're used for the basis of ground truth. As well as singling out problematic ones, possibly incorrect in classification or objects gone completely undetected as a false positive.
After identifying the risks of lower quality instances of auto-generated labels, it's much easier for data annotators and managers to anticipate and properly prepare to catch and resolve them as they occur, preferably - going so far as to prevent their occurrence at all.
The most effective way of doing so, while also aiming for the more time and cost-effective conveniences of automated annotation, is to work from a small dataset and gain enough value from it, which is possible through data augmentation, a series of techniques described below, that function as simple and efficient ways to enlarge datasets.
Technology That Makes Auto-Labeling More Efficient
Difficulty Scoring System
In line with the goal behind assigning an uncertainty score, there's yet another practice available for data labeling teams to adopt, a difficulty scoring system. Working interchangeably as a probability measurement method, difficulty scoring works on a low to high scale. If the score is low, then it's more likely to be an ideal label or annotation. It also indicates to data labeling managers conducting QA or overseeing labeling operations that it's less likely to need review or corrective action to be taken.
Bayesian Deep Learning
To rise to the task and challenge of singling out these lower-quality images or data that is labeled through the model-based method, annotators can utilize Bayesian Deep Learning (BDL) to detect them automatically.
Similar to the deterrent method of consensus labeling, frequently employed for complex labeling tasks and carried out through the participation of multiple labelers; who approve of label accuracy and in turn, how reliable model-based annotations are through a weighted majority vote. BDL doesn't simply label an image by a single class but probabilistic distribution to datasets.
Beyond the value of probability distribution through BDL, when measuring the reliability of an auto-labeling model, it's simpler to think of it from the confidence standpoint, which is one of the most popular and well-recognized methods in the industry. Evaluating annotation quality and model output by how "confident" the ML engineer is in its ability to produce accurate results.
However, confidence doesn't necessarily mean "trust" outright. A more realistic term would be the "uncertainty" of model outputs and statistically measuring the likelihood of the model performing correctly. In this way, with the use of an "uncertainty estimation" measurement, data labeling managers have a number or percentage that can equate to model prediction error.
Superb AI’s own labeling suite offers this very feature - auto-labeling capabilities through our Superb AI platform that utilizes uncertainty estimation AI that is based on Bayesian deep learning that measures automated annotation to speed up active learning workflows, as a result, easing the difficulties famously associated with manual labeling and workflow auditing.
Landmark Approaches To Automated Labeling
When working towards the overall betterment of an AI/ML system, it all starts and ends with the ability to generate the best quality data. What counts as the "best" data is largely dependent on a model's use case and how easy labelers and data managers find it to produce it for the iterative and unique needs of each individual model.
With novel approaches like interactive and model-based auto-labeling now accessible, ML teams are capable of generating the most accurate datasets to fuel their training and evaluation goals. Although these approaches are meant to relieve the considerable expense and time dedicated to data preparation, they require proper implementation to follow through on that purpose.
These methods should also be mindfully implemented, according to the parameters of each project or build, as well as the type of data requiring annotation. As data segmentation enables the development of higher-scale models using a small amount of data, the considerable volume required for these applications can still be provided in a cost-effective and efficient manner.
If a labeling team wishes to cut down on repetitive basic annotation tasks, they'd benefit from incorporating an interactive-based labeling approach; while model-based auto-labeling can offer them even more leeway to focus on perfecting the entire data labeling process to a finite degree and stepping in to take corrective actions as-needed.