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Efficient Computation of Loss Functions in Instance Representation Learning

The computation of loss functions, such as Maximum Likelihood Estimation (MLE) and Negative Log-Likelihood (NLL), is challenging in instance representation learning due to the large number of images in the dataset. This leads to a bottleneck in the training process. Researchers are seeking more efficient methods to approximate these loss functions.

Computing & Technology, Computer Science, Machine Learning