Search Results for author: Kun Bai

Found 24 papers, 11 papers with code

Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies

1 code implementation28 Aug 2022 Yinghua Zhang, Yangqiu Song, Kun Bai, Qiang Yang

To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example.

Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets

no code implementations12 Jul 2022 Yingsong Huang, Bing Bai, Shengwei Zhao, Kun Bai, Fei Wang

The second issue refers to that models may output misleading predictions due to epistemic uncertainty and aleatoric uncertainty, thus existing methods that rely solely on the output probabilities may fail to distinguish confident samples.

Contrastive Multi-view Hyperbolic Hierarchical Clustering

no code implementations5 May 2022 Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, Zenglin Xu

Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.

AFINet: Attentive Feature Integration Networks for Image Classification

no code implementations10 May 2021 Xinglin Pan, Jing Xu, Yu Pan, Liangjian Wen, WenXiang Lin, Kun Bai, Zenglin Xu

Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification.

Classification General Classification +1

MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance

1 code implementation25 Jan 2021 Jing Xu, Tszhang Guo, Yong Xu, Zenglin Xu, Kun Bai

Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently.

Face Recognition

Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark

no code implementations15 Oct 2020 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Conghui Zhu, Tiejun Zhao

Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.

Natural Language Inference

Domain Agnostic Learning for Unbiased Authentication

no code implementations11 Oct 2020 Jian Liang, Yuren Cao, Shuang Li, Bing Bai, Hao Li, Fei Wang, Kun Bai

We further extend our method to a meta-learning framework to pursue more thorough domain-difference elimination.

Face Recognition Meta-Learning +1

Hybrid Differentially Private Federated Learning on Vertically Partitioned Data

no code implementations6 Sep 2020 Chang Wang, Jian Liang, Mingkai Huang, Bing Bai, Kun Bai, Hao Li

We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized linear model (GLM) from vertically partitioned data with only a negligible cost, w. r. t.

Federated Learning Privacy Preserving

A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations

no code implementations25 Aug 2020 Mingkai Huang, Hao Li, Bing Bai, Chang Wang, Kun Bai, Fei Wang

Federated Learning(FL) is a newly developed privacy-preserving machine learning paradigm to bridge data repositories without compromising data security and privacy.

Collaborative Filtering Federated Learning +1

Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning

1 code implementation25 Aug 2020 Yinghua Zhang, Yangqiu Song, Jian Liang, Kun Bai, Qiang Yang

To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model.

Transfer Learning

Relation-Guided Representation Learning

no code implementations11 Jul 2020 Zhao Kang, Xiao Lu, Jian Liang, Kun Bai, Zenglin Xu

In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning.

Representation Learning

Why Attentions May Not Be Interpretable?

no code implementations10 Jun 2020 Bing Bai, Jian Liang, Guanhua Zhang, Hao Li, Kun Bai, Fei Wang

In this paper, we demonstrate that one root cause of this phenomenon is the combinatorial shortcuts, which means that, in addition to the highlighted parts, the attention weights themselves may carry extra information that could be utilized by downstream models after attention layers.

Feature Importance

Adversarial Infidelity Learning for Model Interpretation

1 code implementation9 Jun 2020 Jian Liang, Bing Bai, Yuren Cao, Kun Bai, Fei Wang

A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature representing the data samples to explain how the model generates the specific output.

General-Purpose User Embeddings based on Mobile App Usage

1 code implementation27 May 2020 Junqi Zhang, Bing Bai, Ye Lin, Jian Liang, Kun Bai, Fei Wang

In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage.

Feature Engineering

Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting

1 code implementation ACL 2020 Guanhua Zhang, Bing Bai, Junqi Zhang, Kun Bai, Conghui Zhu, Tiejun Zhao

In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution.

Abusive Language General Classification +3

CSRN: Collaborative Sequential Recommendation Networks for News Retrieval

no code implementations7 Apr 2020 Bing Bai, Guanhua Zhang, Ye Lin, Hao Li, Kun Bai, Bo Luo

Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items.

Collaborative Filtering News Retrieval +2

Fisher Deep Domain Adaptation

1 code implementation12 Mar 2020 Yinghua Zhang, Yu Zhang, Ying WEI, Kun Bai, Yangqiu Song, Qiang Yang

Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.

Domain Adaptation

Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration

no code implementations IJCNLP 2019 Zhufeng Pan, Kun Bai, Yan Wang, Lianqiang Zhou, Xiaojiang Liu

To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context.

Scale Matters: Temporal Scale Aggregation Network for Precise Action Localization in Untrimmed Videos

no code implementations2 Aug 2019 Guoqiang Gong, Liangfeng Zheng, Kun Bai, Yadong Mu

Our proposed TSA-Net demonstrates clear and consistent better performances and re-calibrates new state-of-the-art on both benchmarks.

Temporal Action Localization

Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets

2 code implementations ACL 2019 Guanhua Zhang, Bing Bai, Jian Liang, Kun Bai, Shiyu Chang, Mo Yu, Conghui Zhu, Tiejun Zhao

Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.

Selection bias

Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

1 code implementation NIPS Workshop CDNNRIA 2018 Yu Pan, Jing Xu, Maolin Wang, Jinmian Ye, Fei Wang, Kun Bai, Zenglin Xu

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.

Action Recognition Tensor Decomposition

Improving Reinforcement Learning Based Image Captioning with Natural Language Prior

1 code implementation EMNLP 2018 Tszhang Guo, Shiyu Chang, Mo Yu, Kun Bai

Recently, Reinforcement Learning (RL) approaches have demonstrated advanced performance in image captioning by directly optimizing the metric used for testing.

Image Captioning reinforcement-learning

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