Weusedtheentireimagesthatthe CUBdataset has (train, val, andtest). For example, onthe CUBdataset, theperformancegain (fork =5) is 0.249, 1.035, and 2.276for miniImageNet, tieredImageNet, and ImageNet, respectively.

V olume-wise labeling in 3D medical images is a time-consuming task that requires expertise. As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data.

Neural image compression has made a great deal of progress. State-of-the-art models are based on variational autoencoders and are outperforming classical models.

SSL based on persistent homology (PH), a mathematical tool for modeling topological features of data that persist across multiple scales.

Based on our framework, we also present a hybrid of DRL and EMalgorithms thathasaguaranteed convergencerate.

Such a problem seriously hinders the realistic landing of classical SSL.

This begs the question: Can SSL algorithms simultaneously improve upon both UL and SL?