Researchers leverage new machine learning methods to learn from noisy labels for image classification – Science X

The rapid development of deep learning in recent years is largely due to the rapid increase in the scale of data. The availability of large amounts of data is revolutionary for model training by the deep learning community. With the increase in the amount of data, the scale of mainstream datasets in deep learning is also increasing. For example, the ImageNet dataset contains more than 14 million samples. In this case, larger and more complex models, such as deeper and wider convolutional layers or special network structures, are needed. This motivation basically marks the beginning of a new era of deep learning.

It is worth noting that although large-scale datasets with precise labels (all images have correct labels) are of great importance, the annotation process is rather tedious, which requires long-term human labor and huge financial investment. A common and less expensive way to collect large datasets is through online search engines. As shown in Figure 1, using the search…

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