no code implementations • 19 Feb 2024 • Aiwei Liu, Haoping Bai, Zhiyun Lu, Xiang Kong, Simon Wang, Jiulong Shan, Meng Cao, Lijie Wen
In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF.
1 code implementation • 11 Oct 2023 • Zhengfeng Lai, Haotian Zhang, BoWen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah, Yinfei Yang, Meng Cao
For example, VeCLIP achieves up to +25. 2% gain in COCO and Flickr30k retrieval tasks under the 12M setting.
no code implementations • 13 Jun 2023 • Haoping Bai, Shancong Mou, Tatiana Likhomanenko, Ramazan Gokberk Cinbis, Oncel Tuzel, Ping Huang, Jiulong Shan, Jianjun Shi, Meng Cao
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
no code implementations • 24 Feb 2023 • Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, Jianjun Shi
In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted.
no code implementations • 28 Mar 2022 • Shancong Mou, Meng Cao, Haoping Bai, Ping Huang, Jianjun Shi, Jiulong Shan
To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.
no code implementations • 22 Nov 2021 • Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan
On active learning task, our method achieves 97. 0% Top-1 Accuracy on CIFAR10 with 0. 1% annotated data, and 83. 9% Top-1 Accuracy on CIFAR100 with 10% annotated data.
no code implementations • NeurIPS 2021 • Haoping Bai, Meng Cao, Ping Huang, Jiulong Shan
While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space.
Hardware Aware Neural Architecture Search Model Optimization +2
1 code implementation • CVPR 2020 • Yuan Gao, Haoping Bai, Zequn Jie, Jiayi Ma, Kui Jia, Wei Liu
We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL).
1 code implementation • 11 Mar 2020 • Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, Leman Akoglu
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
2 code implementations • 8 Feb 2020 • Yue Zhao, Xueying Ding, Jianing Yang, Haoping Bai
In this study, we propose a three-module acceleration framework called SUOD to expedite the training and prediction with a large number of unsupervised detection models.