1 code implementation • 26 Feb 2024 • Hao Wang, Zeyu Gao, Chao Zhang, Zihan Sha, Mingyang Sun, Yuchen Zhou, Wenyu Zhu, Wenju Sun, Han Qiu, Xi Xiao
At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code.
no code implementations • 28 Oct 2023 • Wenju Sun, Qingyong Li, Wen Wang, Yangli-ao Geng
The knowledge from the plastic learner is transferred to the stable learner via cumulative parameter averaging.
1 code implementation • CVPR 2023 • Wenju Sun, Qingyong Li, Jing Zhang, Wen Wang, Yangli-ao Geng
BMKP decouples the functions of learning and knowledge remembering via a bilevel-memory design: a working memory responsible for adaptively model learning, to ensure plasticity; a long-term memory in charge of enduringly storing the knowledge incorporated within the learned model, to guarantee stability.
1 code implementation • 5 Jan 2022 • Wenju Sun, Qingyong Li, Jing Zhang, Danyu Wang, Wen Wang, Yangli-ao Geng
DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e. g. deep SVDD), because a trained VAE can not only identify the probability of an input sample belonging to a class but also generate pseudo samples of the class to assist in learning new tasks.