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20172022
most citedRetinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

60 citations · 205 across the 15 of their papers we have counts for

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Showing cs.LGShow all

7 papers · 1 filter

cs.LG20223 cited

Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application

Risheng Liu, Jiaxin Gao, Xuan Liu +1

In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs). Unfortunately,…

cs.LG202113 cited

Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond

Risheng Liu, Yaohua Liu, Shangzhi Zeng +1

In recent years, Bi-Level Optimization (BLO) techniques have received extensive attentions from both learning and vision communities. A variety of BLO models in complex and practic…

cs.LG2021

Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond

Risheng Liu, Jiaxin Gao, Jin Zhang +2

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchi…

cs.LG2020

BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning

Yaohua Liu, Risheng Liu

Meta-learning (a.k.a. learning to learn) has recently emerged as a promising paradigm for a variety of applications. There are now many meta-learning methods, each focusing on diff…

cs.LG202012 cited

A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton

Risheng Liu, Pan Mu, Xiaoming Yuan +2

In recent years, a variety of gradient-based first-order methods have been developed to solve bi-level optimization problems for learning applications. However, theoretical guarant…

cs.LG2018

Exploiting Local Feature Patterns for Unsupervised Domain Adaptation

Jun Wen, Risheng Liu, Nenggan Zheng +3

Unsupervised domain adaptation methods aim to alleviate performance degradation caused by domain-shift by learning domain-invariant representations. Existing deep domain adaptation…