This section reports the results of our QAD and other baselines under two scenarios: 1) quality-agnostic setting, which represents no model has prior knowledge of the input images’ quality, and 2) quality …

In addition, an Adversarial Weight Perturbation module is carefully devised to enable the model to be more robust against image corruption while boosting the overall model's performance. Extensive …

In this paper, we propose a novel approach called qual-ity aware dynamic discriminator rejection sampling (QAD-DRS) to improve DE-GANs training by considering sample quality.

To address the accuracy drop introduced by OSQ, we apply our proposed QAD technique. Using the fully INT8-quantized FMQ model as the teacher and the OSQ model as the student, QAD is applied …

QAD-DRS, we also report the experiment results using another commonly-used GANs evaluation metric, i.e., Inception Score (IS) [4]. The res lts on low-shot datasets compared with the state-of-the …

More recently, QAD [17] has extended to achieve cross-quality DeepFake detection, by employing specific learning strategies (e.g., contrastive learning and collaborative learning) to capture generic …

Abstract Structured pruning and quantization are fundamental techniques used to reduce the size of deep neural networks (DNNs), and typically are applied independently. Applying these techniques …

We compared various baselines including CNNSpot [91], FreDect [25], Fusing [38], LNP [58], LGrad [84], Uni- vFD [66], DIRE [93], PatchCraft [108], NPR [85], An- tiFakePrompt [5], Fatformer [62], …

4Peking University,5Microsoft,6Canva https://hybrid-layout-msra.github.io Figure 1. Illustrating the layout control results with our approach. Our approach not only ensures accurate adherence to the layout …

Abstract Multimodal sentiment analysis stand human sentiment through MSA efforts are based on the assumption pleteness. However, in real-world tical factors cause uncertain modality drastically …