Alternatives to curriculum learning

Curriculum learning is a training paradigm where samples are presented to a model in a specific, non-random order, starting with simpler examples and gradually introducing more complex ones. This strategy is inspired by how humans learn and can lead to faster convergence and better final performance, especially in complex tasks.

At a glance

Executive summary

Curriculum learning is a training strategy that presents data to a machine learning model in a meaningful order, typically from easy to hard. It aims to improve convergence speed and generalization performance by mimicking human learning processes. This approach is a specialized technique within the broader field of machine learning, distinct from general methods like pseudo-labeling or graph-based approaches.

TL;DR

If you need to guide model learning with ordered data, use curriculum learning; if you need to leverage unlabeled data, use pseudo-labeling; if you need to exploit data relationships, use graph-based methods; if you need a general framework, use machine learning.

Key points

Consider curriculum learning when you have a natural ordering of data difficulty or can define one.
Choose pseudo-labeling when you have a large amount of unlabeled data and a smaller labeled dataset.
Opt for graph-based methods when your data has inherent relational structures that can be represented as a graph.
Evaluate curriculum learning's effectiveness against simpler baseline machine learning approaches before implementation.
Compare curriculum learning with pseudo-labeling and graph-based methods if your problem involves both unlabeled data and relational information.

Our Take

## Our Take In the evolving landscape of machine learning, both curriculum learning and pseudo-labeling have emerged as pivotal strategies to enhance model performance, albeit through distinct methodologies. Curriculum learning, as proposed by Bengio et al. (2009), advocates for training models on progressively complex tasks, which mirrors human learning and has been shown to improve convergence rates and final accuracy in various domains, including natural language processing and computer vision. For instance, Karras et al. (2019) demonstrated that a curriculum approach significantly improved the training of GANs, leading to more realistic image generation. On the other hand, pseudo-labeling, which involves using a model’s predictions as labels for unlabeled data, has gained traction in semi-supervised learning. This technique, highlighted by Lee (2013), leverages the abundance of unlabeled data to enhance model training, often resulting in superior performance when combined with traditional supervised learning. Notably, the work of Xie et al. (2020) illustrated that pseudo-labeling could yield state-of-the-art results in image classification tasks by effectively utilizing unlabeled datasets. Graph-based methods, such as those explored by Kipf and Welling (2017), offer an alternative by modeling relationships between data points, which can be particularly beneficial in scenarios with complex interdependencies. These methods excel in tasks like social network analysis and recommendation systems, where the structure of the data plays a crucial role in learning. In summary, while curriculum learning focuses on the sequence of task complexity to enhance learning efficiency, pseudo-labeling maximizes the utility of unlabeled data, and graph-based methods exploit relational structures. Each approach has its merits and can be synergistically combined, suggesting a promising direction for future research in machine learning.

Alternative	Difference	Papers (with curriculum learning)	Avg viability
pseudo-labeling	—	1	—
graph-based methods	—	1	—
machine learning	—	1	—

Alternative

Difference

Papers (with curriculum learning)

Avg viability

pseudo-labeling

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graph-based methods

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machine learning

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