Data Ablation

Data Ablation refers to the process of studying how changes in data affect the performance of neural networks. By systematically removing or altering parts of the dataset, this method aims to evaluate how these changes impact the model's training effectiveness and predictive capabilities, thereby helping to identify key data features and optimize data processing strategies. In the field of computer vision, Data Ablation helps to understand which parts of image data are most critical for model performance, thus enhancing the model's robustness and generalization ability.