7 citations · 8 across the 5 of their papers we have counts for
9 papers
A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework
Munan Ning, Cheng Bian, Dong Wei +5
Domain shift happens in cross-domain scenarios commonly because of the wide gaps between different domains: when applying a deep learning model well-trained in one domain to anothe…
Fine-Grained AutoAugmentation for Multi-Label Classification
Ya Wang, Hesen Chen, Fangyi Zhang +4
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better…
CODIC: A Low-Cost Substrate for Enabling Custom In-DRAM Functionalities and Optimizations
Lois Orosa, Yaohua Wang, Mohammad Sadrosadati +10
DRAM is the dominant main memory technology used in modern computing systems. Computing systems implement a memory controller that interfaces with DRAM via DRAM commands. DRAM exec…
FIGARO: Improving System Performance via Fine-Grained In-DRAM Data Relocation and Caching
Yaohua Wang, Lois Orosa, Xiangjun Peng +8
DRAM Main memory is a performance bottleneck for many applications due to the high access latency. In-DRAM caches work to mitigate this latency by augmenting regular-latency DRAM w…
A Macro-Micro Weakly-supervised Framework for AS-OCT Tissue Segmentation
Munan Ning, Cheng Bian, Donghuan Lu +7
Primary angle closure glaucoma (PACG) is the leading cause of irreversible blindness among Asian people. Early detection of PACG is essential, so as to provide timely treatment and…
Hierarchical Clustering with Hard-batch Triplet Loss for Person Re-identification
Kaiwei Zeng
For most unsupervised person re-identification (re-ID), people often adopt unsupervised domain adaptation (UDA) method. UDA often train on the labeled source dataset and evaluate o…