Improving Data Efficiency in Machine Learning through Semantic Duplication Detection

The Growth of Self-Supervised Learning in AI

Self-supervised learning (SSL) has played a crucial role in the success of machine learning by applying it to larger models and unlabeled datasets. One particular dataset, called LAION, has been instrumental in this growth, containing 5 billion image/text pairs.

The Effect of Data Size on Test Error

Test error tends to follow a power law relationship with data size. As more data is added, the performance improvements become smaller and smaller. This diminishing marginal return highlights the need for improved data efficiency. If models can achieve the same performance faster or better with the same computational budget, it would have a significant impact.

The Three Types of Data Outliers

Recent studies are exploring the best ways to select data to improve efficiency. These methods focus on three groups of outliers ranked by difficulty:

1. Perceptual duplicates: data pairs that look almost identical.
2. Semantic duplicates: data with similar information content but easily distinguishable.
3. Semantic redundancy: data with repeated information but not identical.

The Importance of Removing Misleading Data

In addition to the three types of outliers, misleading data that negatively affect performance can also be removed. By eliminating these duplicates and misleading data, training can be accelerated or achieve better results with the same amount of data.

SemDeDup: A Method to Identify Semantic Duplicates

Researchers from Meta AI and Stanford University have developed SemDeDup, a simple and computationally tractable method to detect semantic duplicates. It focuses on identifying semantically identical data that is difficult to differentiate using traditional deduplication algorithms. By utilizing k-means clustering on a pre-trained model, nearby residents falling below a given cutoff can be identified.

Efficiency Gains with SemDeDup

Applying SemDeDup to the LAION training set, researchers were able to shrink the dataset by half with minimal performance loss. This resulted in faster learning and comparable or better results outside of distribution tasks. In experiments on the C4 text corpus, SemDeDup achieved efficiency gains of 15% while outperforming previous deduplication methods.

Reducing Dataset Size for Faster Training

While removing semantic duplicates is a good starting point for reducing data size, it is not the only option. The ultimate goal is to have much smaller datasets, leading to shorter training times and increased accessibility to massive models.


The growth of self-supervised learning and the development of methods like SemDeDup have shown promise in improving data efficiency for AI models. By removing redundant and misleading data, training can be accelerated without sacrificing performance. The future of AI lies in creating smaller datasets that can still achieve impressive results.

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