Efficient Deep Representation Learning by Adaptive Latent Space Sampling
This addresses the challenge of reducing annotation costs and improving training efficiency for deep learning practitioners, though it is incremental as it builds on existing sampling and generative model techniques.
The paper tackles the problem of expensive annotations and loss of sample informativeness in supervised deep learning by proposing a training framework that adaptively selects informative samples based on a hardness-aware strategy in the latent space, resulting in outperforming random sampling on MNIST, CIFAR-10, and a medical image dataset.
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain. During the training of a deep neural network, the annotated samples are fed into the network in a mini-batch way, where they are often regarded of equal importance. However, some of the samples may become less informative during training, as the magnitude of the gradient start to vanish for these samples. In the meantime, other samples of higher utility or hardness may be more demanded for the training process to proceed and require more exploitation. To address the challenges of expensive annotations and loss of sample informativeness, here we propose a novel training framework which adaptively selects informative samples that are fed to the training process. The adaptive selection or sampling is performed based on a hardness-aware strategy in the latent space constructed by a generative model. To evaluate the proposed training framework, we perform experiments on three different datasets, including MNIST and CIFAR-10 for image classification task and a medical image dataset IVUS for biophysical simulation task. On all three datasets, the proposed framework outperforms a random sampling method, which demonstrates the effectiveness of proposed framework.