Search Results for author: Liqun Chen

Found 34 papers, 12 papers with code

Jointly Optimizing Image Compression with Low-light Image Enhancement

no code implementations24 May 2023 Shilv Cai, Xu Zou, Liqun Chen, Luxin Yan, Sheng Zhong

To simultaneously achieve a higher compression rate and better enhancement performance for low-light images, we propose a novel image compression framework with joint optimization of low-light image enhancement.

Image Compression Low-Light Image Enhancement

High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation

1 code implementation12 Sep 2022 Shilv Cai, Zhijun Zhang, Liqun Chen, Luxin Yan, Sheng Zhong, Xu Zou

We implement the IAT in a mathematical invertible manner on a single rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors.

Image Compression Vocal Bursts Intensity Prediction

Multi-modal Alignment using Representation Codebook

no code implementations CVPR 2022 Jiali Duan, Liqun Chen, Son Tran, Jinyu Yang, Yi Xu, Belinda Zeng, Trishul Chilimbi

Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion.

Representation Learning Retrieval

Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing

no code implementations1 Dec 2021 Yanjie Wang, Xu Zou, Zhijun Zhang, Wenhui Xu, Liqun Chen, Sheng Zhong, Luxin Yan, Guodong Wang

Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images.

object-detection Object Detection +2

SpanPredict: Extraction of Predictive Document Spans with Neural Attention

no code implementations NAACL 2021 Vivek Subramanian, Matthew Engelhard, Sam Berchuck, Liqun Chen, Ricardo Henao, Lawrence Carin

In many natural language processing applications, identifying predictive text can be as important as the predictions themselves.


Towards Robust and Efficient Contrastive Textual Representation Learning

no code implementations1 Jan 2021 Liqun Chen, Yizhe Zhang, Dianqi Li, Chenyang Tao, Dong Wang, Lawrence Carin

There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence.

Contrastive Learning Representation Learning

Wasserstein Contrastive Representation Distillation

no code implementations CVPR 2021 Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin

The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former.

Contrastive Learning Knowledge Distillation +2

Contextualized Perturbation for Textual Adversarial Attack

1 code implementation NAACL 2021 Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, Bill Dolan

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.

Adversarial Attack Language Modelling

Weakly supervised cross-domain alignment with optimal transport

no code implementations14 Aug 2020 Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin

Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.

Graph Optimal Transport for Cross-Domain Alignment

1 code implementation ICML 2020 Liqun Chen, Zhe Gan, Yu Cheng, Linjie Li, Lawrence Carin, Jingjing Liu

In GOT, cross-domain alignment is formulated as a graph matching problem, by representing entities into a dynamically-constructed graph.

Graph Matching Image Captioning +7

Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

no code implementations20 Nov 2019 Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.

Multi-Task Learning Type prediction

LMVP: Video Predictor with Leaked Motion Information

no code implementations24 Jun 2019 Dong Wang, Yitong Li, Wei Cao, Liqun Chen, Qi Wei, Lawrence Carin

We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs.

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models

no code implementations ACL 2019 Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation with latent variables.

Text Generation

Sequence Generation with Guider Network

no code implementations2 Nov 2018 Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Liqun Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin

Sequence generation with reinforcement learning (RL) has received significant attention recently.

Reinforcement Learning (RL)

Hierarchically-Structured Variational Autoencoders for Long Text Generation

no code implementations27 Sep 2018 Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Lawrence Carin

Variational autoencoders (VAEs) have received much attention recently as an end-to-end architecture for text generation.

Text Generation

Chi-square Generative Adversarial Network

1 code implementation ICML 2018 Chenyang Tao, Liqun Chen, Ricardo Henao, Jianfeng Feng, Lawrence Carin Duke

To assess the difference between real and synthetic data, Generative Adversarial Networks (GANs) are trained using a distribution discrepancy measure.

Variational Inference and Model Selection with Generalized Evidence Bounds

no code implementations ICML 2018 Liqun Chen, Chenyang Tao, Ruiyi Zhang, Ricardo Henao, Lawrence Carin Duke

Recent advances on the scalability and flexibility of variational inference have made it successful at unravelling hidden patterns in complex data.

Model Selection Variational Inference

A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

no code implementations29 May 2018 Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis.

Adversarial Symmetric Variational Autoencoder

no code implementations NeurIPS 2017 Yunchen Pu, Wei-Yao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin

A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: ($i$) from observed data fed through the encoder to yield codes, and ($ii$) from latent codes drawn from a simple prior and propagated through the decoder to manifest data.

Triangle Generative Adversarial Networks

1 code implementation NeurIPS 2017 Zhe Gan, Liqun Chen, Wei-Yao Wang, Yunchen Pu, Yizhe Zhang, Hao liu, Chunyuan Li, Lawrence Carin

The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.

Image-to-Image Translation Semi-Supervised Image Classification +1

Symmetric Variational Autoencoder and Connections to Adversarial Learning

2 code implementations6 Sep 2017 Liqun Chen, Shuyang Dai, Yunchen Pu, Chunyuan Li, Qinliang Su, Lawrence Carin

A new form of the variational autoencoder (VAE) is proposed, based on the symmetric Kullback-Leibler divergence.

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

5 code implementations NeurIPS 2017 Chunyuan Li, Hao liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching.

Continuous-Time Flows for Efficient Inference and Density Estimation

no code implementations ICML 2018 Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin

Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees.

Density Estimation

Formal Analysis of V2X Revocation Protocols

no code implementations24 Apr 2017 Jorden Whitefield, Liqun Chen, Frank Kargl, Andrew Paverd, Steve Schneider, Helen Treharne, Stephan Wesemeyer

This paper focusses on the formal analysis of a particular element of security mechanisms for V2X found in many proposals: the revocation of malicious or misbehaving vehicles from the V2X system by invalidating their credentials.

Cryptography and Security D.2.4; D.4.6

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