68 citations · 103 across the 8 of their papers we have counts for
8 papers
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
Xiao Zhou, Yong Lin, Renjie Pi +4
Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two popular methods proposed to improve out-of-distribution (OOD) generalization performance of…
G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation
Lewei Yao, Renjie Pi, Hang Xu +3
In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous stud…
Joint-DetNAS: Upgrade Your Detector with NAS, Pruning and Dynamic Distillation
Lewei Yao, Renjie Pi, Hang Xu +3
We propose Joint-DetNAS, a unified NAS framework for object detection, which integrates 3 key components: Neural Architecture Search, pruning, and Knowledge Distillation. Instead o…
TransNAS-Bench-101: Improving Transferability and Generalizability of Cross-Task Neural Architecture Search
Yawen Duan, Xin Chen, Hang Xu +4
Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existin…
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Bochao Wang, Hang Xu, Jiajin Zhang +21
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated Auto…
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search
Xin Chen, Yawen Duan, Zewei Chen +5
Neural Architecture Search (NAS) achieved many breakthroughs in recent years. In spite of its remarkable progress, many algorithms are restricted to particular search spaces. They…
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Han Shi, Renjie Pi, Hang Xu +3
Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the sea…
Hierarchical Neural Architecture Search via Operator Clustering
Guilin Li, Xing Zhang, Zitong Wang +4
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS. However, recent literature has brou…