866 citations · 1.4k across the 20 of their papers we have counts for
16 papers · 1 filter
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
Tianlong Chen, Jonathan Frankle, Shiyu Chang +4
The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervis…
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Haotao Wang, Tianlong Chen, Shupeng Gui +3
Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. Moreover, the training process is heavy…
Robust Pre-Training by Adversarial Contrastive Learning
Ziyu Jiang, Tianlong Chen, Ting Chen +1
Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness In this work, we improve robustness-awar…
Training Stronger Baselines for Learning to Optimize
Tianlong Chen, Weiyi Zhang, Jingyang Zhou +4
Learning to optimize (L2O) has gained increasing attention since classical optimizers require laborious problem-specific design and hyperparameter tuning. However, there is a gap b…
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
Yuli Zheng, Zhenyu Wu, Ye Yuan +2
Credit risk modeling has permeated our everyday life. Most banks and financial companies use this technique to model their clients' trustworthiness. While machine learning is incre…
Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui +3
Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been develop…
When Does Self-Supervision Help Graph Convolutional Networks?
Yuning You, Tianlong Chen, Zhangyang Wang +1
Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning…
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
Tianlong Chen, Jonathan Frankle, Shiyu Chang +4
In natural language processing (NLP), enormous pre-trained models like BERT have become the standard starting point for training on a range of downstream tasks, and similar trends…
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…
Can 3D Adversarial Logos Cloak Humans?
Yi Wang, Jingyang Zhou, Tianlong Chen +4
With the trend of adversarial attacks, researchers attempt to fool trained object detectors in 2D scenes. Among many of them, an intriguing new form of attack with potential real-w…
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification
Prateek Shroff, Tianlong Chen, Yunchao Wei +1
Deep Neural Network has shown great strides in the coarse-grained image classification task. It was in part due to its strong ability to extract discriminative feature representati…
AutoSpeech: Neural Architecture Search for Speaker Recognition
Shaojin Ding, Tianlong Chen, Xinyu Gong +2
Speaker recognition systems based on Convolutional Neural Networks (CNNs) are often built with off-the-shelf backbones such as VGG-Net or ResNet. However, these backbones were orig…
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Tianlong Chen, Sijia Liu, Shiyu Chang +3
Pretrained models from self-supervision are prevalently used in fine-tuning downstream tasks faster or for better accuracy. However, gaining robustness from pretraining is left une…
L-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Yuning You, Tianlong Chen, Zhangyang Wang +1
Graph convolution networks (GCN) are increasingly popular in many applications, yet remain notoriously hard to train over large graph datasets. They need to compute node representa…
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
Haotao Wang, Tianlong Chen, Zhangyang Wang +1
The learning of hierarchical representations for image classification has experienced an impressive series of successes due in part to the availability of large-scale labeled data…
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Ting-Kuei Hu, Tianlong Chen, Haotao Wang +1
Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019). Such a…