The recent advancements in the research area primarily focus on enhancing the robustness and efficiency of machine learning models, particularly in the presence of noisy or incomplete data. A significant trend is the integration of self-supervised learning and iterative refinement techniques to mitigate the impact of instance-dependent label noise, which is more prevalent and challenging to address than instance-independent noise. These methods leverage robust feature representations learned without relying on potentially noisy labels, followed by iterative pseudo-label refinement to progressively improve label quality. Another notable direction is the exploration of cost-effective annotation strategies for object detection, questioning the necessity of annotating small-size instances and proposing alternative methods to achieve comparable performance without such annotations. Additionally, there is a growing emphasis on developing robust training strategies for models dealing with noisy correspondence and partial label learning, often employing novel frameworks that partition data into different types and adaptively leverage significant samples. These developments collectively aim to advance the field by enabling more reliable and efficient training of deep learning models in real-world scenarios characterized by noisy and incomplete data.