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Found 13 papers, 1 papers with code
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
However, with the increase of minimax optimization and variational inequality problems in machine learning, the necessity of designing efficient distributed/federated learning approaches for these problems is becoming more apparent.
Refined Pseudo labeling for Source-free Domain Adaptive Object Detection
Domain adaptive object detection (DAOD) assumes that both labeled source data and unlabeled target data are available for training, but this assumption does not always hold in real-world scenarios.
FIT: Frequency-based Image Translation for Domain Adaptive Object Detection
Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain.
Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources
When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel.
Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization
We further study the algorithm-dependent generalization bounds via stability arguments of algorithms.
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation
In this paper, we emphasize the adaptation process across sim2real domains and model it as a learning problem on the BatchNorm parameters of a simulation-trained model.
A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation
Micro-expressions are spontaneous, unconscious facial movements that show people's true inner emotions and have great potential in related fields of psychological testing.
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization
In the averaged smooth finite-sum setting, our proposed algorithm improves over previous algorithms by providing a nearly-tight dependence on the condition number.
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
Conditional stochastic optimization covers a variety of applications ranging from invariant learning and causal inference to meta-learning.
A Catalyst Framework for Minimax Optimization
We introduce a generic \emph{two-loop} scheme for smooth minimax optimization with strongly-convex-concave objectives.
First-Order Optimization Inspired from Finite-Time Convergent Flows
In this paper, we investigate the performance of two first-order optimization algorithms, obtained from forward Euler discretization of finite-time optimization flows.
Biased Stochastic Gradient Descent for Conditional Stochastic Optimization
Conditional Stochastic Optimization (CSO) covers a variety of applications ranging from meta-learning and causal inference to invariant learning.
