Search Results for author: Chaoyu Guan

Found 7 papers, 3 papers with code

Post-training Quantization with Progressive Calibration and Activation Relaxing for Text-to-Image Diffusion Models

no code implementations10 Nov 2023 Siao Tang, Xin Wang, Hong Chen, Chaoyu Guan, Zewen Wu, Yansong Tang, Wenwu Zhu

In this paper, we propose a novel post-training quantization method PCR (Progressive Calibration and Relaxing) for text-to-image diffusion models, which consists of a progressive calibration strategy that considers the accumulated quantization error across timesteps, and an activation relaxing strategy that improves the performance with negligible cost.


Lightweight Diffusion Models with Distillation-Based Block Neural Architecture Search

no code implementations8 Nov 2023 Siao Tang, Xin Wang, Hong Chen, Chaoyu Guan, Yansong Tang, Wenwu Zhu

When retraining the searched architecture, we adopt a dynamic joint loss to maintain the consistency between supernet training and subnet retraining, which also provides informative objectives for each block and shortens the paths of gradient propagation.

Neural Architecture Search

NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results

no code implementations31 Aug 2022 Dustin Carrión-Ojeda, Hong Chen, Adrian El Baz, Sergio Escalera, Chaoyu Guan, Isabelle Guyon, Ihsan Ullah, Xin Wang, Wenwu Zhu

We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning.

Few-Shot Image Classification Few-Shot Learning +1

MetaDelta: A Meta-Learning System for Few-shot Image Classification

1 code implementation22 Feb 2021 Yudong Chen, Chaoyu Guan, Zhikun Wei, Xin Wang, Wenwu Zhu

Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks.

Classification Few-Shot Image Classification +2

Semantic Role Labeling with Associated Memory Network

1 code implementation NAACL 2019 Chaoyu Guan, Yuhao Cheng, Hai Zhao

Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented.

Semantic Role Labeling Sentence

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