Because you are adding more objects for an algorithm to learn, so a given algorithm can get closer to the ground truth and the data is supposed to be a representation of the ground truth. However it is important to denote the fact that there is a cutoff where additional data might not give results that significantly improve the results. At that stage other approaches such as pruning the dataset or changing the architecture of a given solution might benefit it more than that of adding more data into the mix.
The performance of deep learning models tends to improve as more data is fed to them due to several reasons:
1. Increased Model Generalization: Deep learning models have a high capacity to learn complex patterns and representations from data. By providing more diverse and abundant data, the model gets exposed to a wider range of examples and variations, enabling it to learn more generalizable features and make better predictions on unseen data.
2. Reduction of Overfitting: Overfitting occurs when a model becomes too specialized in learning the training data and fails to generalize well to new data. More data helps to mitigate overfitting by providing a larger and more representative sample of the underlying data distribution. As the model observes more instances of different variations, it becomes less likely to memorize specific examples and focuses on learning meaningful patterns.
3. Enhanced Feature Learning: Deep learning models automatically learn hierarchical representations of data through multiple layers of interconnected neurons. The more data the model is exposed to, the better it can capture subtle and complex relationships between features. This enables the model to extract more informative and discriminative features from the input data, leading to improved performance.
4. Noise Reduction: Real-world data often contains noise, outliers, and irrelevant information. When the dataset is small, the model may mistakenly learn from these noisy instances and make incorrect predictions. However, as the amount of data increases, the influence of noise decreases, allowing the model to focus on the true underlying patterns and reducing the impact of irrelevant information.
5. Increased Training Stability: Deep learning models are trained through an optimization process that updates the model's parameters to minimize the discrepancy between predicted and actual outputs. With a larger dataset, the optimization process becomes more stable, as the gradients computed from more examples provide a better estimate of the true underlying gradients. This stability leads to more effective training and convergence to better solutions.
It is important to note that while more data generally improves deep learning performance, there may be diminishing returns. At a certain point, the benefits of additional data may plateau, and other factors such as model architecture, optimization techniques, and data quality become more crucial for further performance gains.