Self-Paced Knowledge Distillation for Real-Time Image Guided Depth ...
ieeexplore.ieee.org › document
Feb 24, 2022 · We propose a self-paced knowledge distillation method, which obtains a lightweight but accurate depth completion model via distilling knowledge from a complex ...
In this letter, we propose a self-paced knowledge distillation method, which obtains a lightweight but accurate depth completion model via distilling knowledge ...
In this letter, we propose a self-paced knowledge distillation method, which obtains a lightweight but accurate depth completion model via distilling knowledge ...
People also ask
What is self distillation in deep learning?
What is the knowledge distillation technique?
In this letter, we propose a self-paced knowledge distillation method, which obtains a lightweight but accurate depth completion model via distilling knowledge ...
This paper introduces a methodology for creating an efficient, high-fidelity depth completion model derived from a base model, and introduces a ...
We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks ...
We proposed a deep knowledge distillation model, tailored to effectively capture spatio-temporal patterns in traffic flow prediction.
Missing: Paced | Show results with:Paced
In this paper, we propose a lightweight depth completion network for depth perception in real-world environments. To effectively transfer a teacher's knowledge, ...
Missing: Paced | Show results with:Paced
As direct su- pervision and a shared backbone lead to a strong training signal, we train the knowledge distillation for only 20,000 steps. 4.2. Depth Prediction.
Knowledge Distillation uses a simpler student model to approximate the function learned by a larger, more complex teacher model by training it to learn the soft ...