  In recent years, representation learning has become the research focus of the
machine learning community. Large-scale pre-training neural networks have
become the first step to realize general intelligence. The key to the success
of neural networks lies in their abstract representation capabilities for data.
Several learning fields are actually discussing how to learn representations
and there lacks a unified perspective. We convert the representation learning
problem under multiple tasks into a ranking problem, taking the ranking problem
as a unified perspective, the representation learning under different tasks is
solved by optimizing the approximate NDCG loss. Experiments under different
learning tasks like classification, retrieval, multi-label learning,
regression, self-supervised learning prove the superiority of approximate NDCG
loss. Further, under the self-supervised learning task, the training data is
transformed by data augmentation method to improve the performance of the
approximate NDCG loss, which proves that the approximate NDCG loss can make
full use of the information of the unsupervised training data.
