114 citations · 337 across the 12 of their papers we have counts for
12 papers
Lifelong Language Pretraining with Distribution-Specialized Experts
Wuyang Chen, Yanqi Zhou, Nan Du +4
Pretraining on a large-scale corpus has become a standard method to build general language models (LMs). Adapting a model to new data distributions targeting different downstream t…
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sheng Shen, Le Hou, Yanqi Zhou +17
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.…
Optical Properties of Crich (C, SiC and FeC) Dust Layered Structure of Massive Stars
Ruiqing Wu, Mengqiu Long, Xiaojiao Zhang +9
The composition and structure of interstellar dust are important and complex for the study of the evolution of stars and the \textbf{interstellar medium} (ISM). However, there is a…
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Xinyu Gong, Wuyang Chen, Yifan Jiang +5
We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards ac…
Automated Synthetic-to-Real Generalization
Wuyang Chen, Zhiding Yu, Zhangyang Wang +1
Models trained on synthetic images often face degraded generalization to real data. As a convention, these models are often initialized with ImageNet pre-trained representation. Ye…
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen, Wuyang Chen, Tianlong Chen +4
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled…
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Yonggan Fu, Wuyang Chen, Haotao Wang +3
The compression of Generative Adversarial Networks (GANs) has lately drawn attention, due to the increasing demand for deploying GANs into mobile devices for numerous applications…
FasterSeg: Searching for Faster Real-time Semantic Segmentation
Wuyang Chen, Xinyu Gong, Xianming Liu +3
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neura…
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
Ye Yuan, Wuyang Chen, Yang Yang +1
The comparative losses (typically, triplet loss) are appealing choices for learning person re-identification (ReID) features. However, the triplet loss is computationally much more…
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Ye Yuan, Wuyang Chen, Tianlong Chen +4
Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification. However, previous ReID methods often failed to address two…
ABD-Net: Attentive but Diverse Person Re-Identification
Tianlong Chen, Shaojin Ding, Jingyi Xie +5
Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor…
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
Wuyang Chen, Ziyu Jiang, Zhangyang Wang +2
Segmentation of ultra-high resolution images is increasingly demanded, yet poses significant challenges for algorithm efficiency, in particular considering the (GPU) memory limits.…