Image inpainting is the task of filling in missing or masked regions of an image with semantically meaningful content. Recent methods have shown significant improvement in dealing with large missing regions. However, these methods usually require large training datasets to achieve satisfactory results, and there has been limited research into training such models on a small number of samples. To address this, we present a novel data-efficient generative residual image inpainting method that produces high-quality inpainting results. The core idea is to use an iterative residual reasoning method that incorporates convolutional neural networks (CNNs) for feature extraction and transformers for global reasoning within generative adversarial networks, along with image-level and patch-level discriminators. We also propose a novel forged-patch adversarial training strategy to create faithful textures and detailed appearances. Extensive evaluation shows that our method outperforms previous methods on the data-efficient image inpainting task, both quantitatively and qualitatively.

Channel pruning can reduce memory consumption and running time with least performance damage, and is one of the most important techniques in network compression. However, existing channel pruning methods mainly focus on the pruning of standard convolutional networks, and they rely intensively on time-consuming fine-tuning to achieve the performance improvement. To this end, we present a novel efficient probability-based channel pruning method for depth-wise separable convolutional networks. Our method leverages a new simple yet effective probability-based channel pruning criterion by taking the scaling and shifting factors of batch normalization layers into consideration. A novel shifting factor fusion technique is further developed to improve the performance of the pruned networks without requiring extra time-consuming fine-tuning. We apply the proposed method to five representative deep learning networks, namely MobileNetV1, MobileNetV2, ShuffleNetV1, ShuffleNetV2, and GhostNet, to demonstrate the efficiency of our pruning method. Extensive experimental results and comparisons on publicly available CIFAR10, CIFAR100, and ImageNet datasets validate the feasibility of the proposed method.