chenkeshuai,keshuai Chen,github

ml-class-assignment

The assignment of machine learning course for post-graduates at BUAA EE, 2019.

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

mlnet-pytorch

Implementation of A Deep Multi-Level Network for Saliency Prediction in Pytorch

modeldistillation-pytorch

PyTorch implementation of "Distilling the Knowledge in a Neural Network" for model compression

object-detection-knowledge-distillation

An Object Detection Knowledge Distillation framework powered by pytorch, now having SSD and yolov5.

omgd

Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

In this study, we propose Perceptual Generative Adversarial Networks (PGANs) for image-to-image transformations. Different from existing application driven algorithms, PGAN provides a generic framework of learning to map from input images to desired images, such as a rainy image to its de-rained counterpart, object edges to photos, and semantic labels to a scenes image. The proposed PAN consists of two feed-forward convolutional neural networks: the image transformation net- work T and the discriminative network D. Besides the generative adversarial loss widely used in GANs, we propose the perceptual adversarial loss, which undergoes an adversarial training process between the image transformation network T and the hidden layers of the discriminative network D. The hidden layers and the output of the discriminative network D are upgraded to constantly and automatically discover the discrepancy between the transformed image and the corresponding ground truth, while the image transformation network T is trained to minimize the discrepancy explored by the discriminative network D. Through integrating the generative adversarial loss and the perceptual adversarial loss, D and T can be trained alternately to solve image-to-image transformation tasks. Experiments evaluated on several image-to-image transformation tasks (e.g., image de- raining and image inpainting) demonstrate the effectiveness of the proposed PAN and its advantages over many existing works.