Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Abstract Knowledge distillation involves transferring soft labels from a teacher to a student
using a shared temperature-based softmax function. However the assumption of a shared …

Most existing distillation methods ignore the flexible role of the temperature in the loss
function and fix it as a hyper-parameter that can be decided by an inefficient grid search. In …

Object detection (OD) is a crucial computer vision task that has seen the development of
many algorithms and models over the years. While the performance of current OD models …

Abstract Knowledge distillation (KD) has witnessed its powerful capability in learning
compact models in object detection. Previous KD methods for object detection mostly focus …

In this paper, we delve into two key techniques in Semi-Supervised Object Detection
(SSOD), namely pseudo labeling and consistency training. We observe that these two …

Abstract Knowledge distillation (KD) is a widely-used technique to train compact models in
object detection. However, there is still a lack of study on how to distill between …

Abstract Knowledge Distillation (KD) has been validated as an effective model compression
technique for learning compact object detectors. Existing state-of-the-art KD methods for …

Abstract Knowledge distillation (KD) has shown potential for learning compact models in
dense object detection. However, the commonly used softmax-based distillation ignores the …

Object detection is a widely applied approach in addressing many real-world computer
vision challenges. Despite its importance, object detection is computationally intensive and …