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Found 14 papers, 4 papers with code
LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow
The visibility of real-world images is often limited by both low-light and low-resolution, however, these issues are only addressed in the literature through Low-Light Enhancement (LLE) and Super- Resolution (SR) methods.
CLeaRForecast: Contrastive Learning of High-Purity Representations for Time Series Forecasting
Time series forecasting (TSF) holds significant importance in modern society, spanning numerous domains.
MoE-AMC: Enhancing Automatic Modulation Classification Performance Using Mixture-of-Experts
Utilizing the MoE framework, MoE-AMC seamlessly combines the strengths of LSRM (a Transformer-based model) for handling low SNR signals and HSRM (a ResNet-based model) for high SNR signals.
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications.
DSAC-T: Distributional Soft Actor-Critic with Three Refinements
Reinforcement learning (RL) has proven to be highly effective in tackling complex decision-making and control tasks.
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments
Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention.
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications
The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-world vision applications, demands a deeper understanding of their underlying coupling mechanisms.
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Long-term time series forecasting (LTSF) is a crucial aspect of modern society, playing a pivotal role in facilitating long-term planning and developing early warning systems.
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation
In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of segmentation models against natural degradation factors (e. g., rain streaks) or artificially attack factors (e. g., adversarial attack).
NAI$_2$: Learning Noise-Aware Illuminance-Interpolator for Unsupervised Low-Light Image Enhancement
Low-light situations severely restrict the pursuit of aesthetic quality in consumer photography.
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understanding.
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.
Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application
In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs).
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community.
