Neural Compatibility Modeling With Probabilistic Knowledge Distillation
By Xianjing Han, Xuemeng Song, Yiyang Yao, Xin-Shun Xu, Liqiang Nie. 2020
44 citations recorded by the source corpus.
In modern society, clothing matching plays a pivotal role in people’s daily life, as suitable outfits can beautify their appearance directly. Nevertheless, how to make a suitable outfit has become a daily headache for many people, especially those who do not have much sense of aesthetics. In the light of this, many research efforts have been dedicated to the task of complementary clothing matching and have achieved great success relying on the advanced data-driven neural networks. However, most existing methods overlook the rich valuable knowledge accumulated by our human beings in the fashion domain, especially the rules regarding clothing matching, like “coats go with dresses” and “silk tops cannot go with chiffon bottoms”. Towards this end, in this work, we propose a knowledge-guided neural compatibility modeling scheme, which is able to incorporate the rich fashion domain knowledge to enhance the performance of the compatibility modeling in the context of clothing matching. To better integrate the huge and implicit fashion domain knowledge into the data-driven neural networks, we present a probabilistic knowledge distillation (PKD) method, which is able to encode vast knowledge rules in a probabilistic manner. Extensive experiments on two real-world datasets have verified the guidance of rules from different sources and demonstrated the effectiveness and portability of our model. As a byproduct, we released the codes and involved parameters to benefit the research community.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Computer Vision and Pattern Recognition
Citations in the corpus
- Learning with Recoverable Forgetting (2022, neutral): “Here, we mainly discuss the related works in knowledge distillation [15,14,54], which trains a student model of a compact size by learning from a larger teacher model or a set of teachers handling the same task.”
- KT-GAN: Knowledge-Transfer Generative Adversarial Network for Text-to-Image Synthesis (2020, background): “Knowledge Distillation (KD) [15], [34] with neural networks was pioneered by Hinton [8], which is a transfer learning method that aims to improve the training of a student network by relying on knowledge borrowed from a powerful teacher network.”