DeNoising Training

Denoising training aims to improve the robustness and performance of machine learning models by incorporating noise handling directly into the training process. Current research focuses on adapting this technique to various model architectures, including transformers and convolutional neural networks, and addressing challenges like training-inference discrepancies and the instability of matching algorithms in tasks such as object detection and text spotting. This approach holds significant promise for enhancing the accuracy and efficiency of models across diverse applications, from image processing and natural language processing to recommendation systems and voice conversion, particularly in scenarios with noisy or unreliable data.