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Training a Task-Specific Image Reconstruction Loss
v1v2 (latest)

Training a Task-Specific Image Reconstruction Loss

IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2021
26 March 2021
Aamir Mustafa
A. Mikhailiuk
Dan-Andrei Iliescu
Varun Babbar
Rafał K. Mantiuk
    DRLSupR
ArXiv (abs)PDFHTML

Papers citing "Training a Task-Specific Image Reconstruction Loss"

10 / 10 papers shown
Title
OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution
OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution
Zhiqiang Wu
Zhaomang Sun
Tong Zhou
Bingtao Fu
Ji Cong
Yitong Dong
Huaqi Zhang
Xuan Tang
Mingsong Chen
Xian Wei
DiffM
24
0
0
11 Aug 2025
Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization
Map Feature Perception Metric for Map Generation Quality Assessment and Loss Optimization
Chenxing Sun
Jing Bai
EGVM
179
1
0
30 Mar 2025
ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
Chau Pham
Juan C. Caicedo
Bryan A. Plummer
141
2
0
25 Mar 2025
Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions
Jinghan Zhang
Henry Xie
Xinhao Zhang
Kunpeng Liu
152
3
0
04 Nov 2024
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A
  Multiobjective Hyperparameter and Architecture Optimization Approach
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Yixuan Sun
O. Sowunmi
Romain Egele
S. Narayanan
Luke Van Roekel
Dali Wang
152
6
0
07 Apr 2024
LuminanceL1Loss: A loss function which measures percieved brightness and
  colour differences
LuminanceL1Loss: A loss function which measures percieved brightness and colour differences
Dominic De Jonge
100
0
0
08 Nov 2023
Direct Unsupervised Denoising
Direct Unsupervised Denoising
Benjamin Salmon
Alexander Krull
DiffMOOD
96
3
0
27 Oct 2023
Comparing the Robustness of Modern No-Reference Image- and Video-Quality
  Metrics to Adversarial Attacks
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial AttacksAAAI Conference on Artificial Intelligence (AAAI), 2023
Anastasia Antsiferova
Khaled Abud
Aleksandr Gushchin
E. Shumitskaya
Sergey Lavrushkin
D. Vatolin
253
16
0
10 Oct 2023
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image
  Signal Processing
ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal ProcessingACM Computing Surveys (ACM Comput. Surv.), 2023
Matheus Henrique Marques da Silva
Jhessica Victoria Santos da Silva
Rodrigo Reis Arrais
Wladimir Barroso Guedes de Araújo Neto
Leonardo Tadeu Lopes
...
Lucas B. Rondon
Bruno Melo de Souza
Mayara Costa Regazio
Rodolfo Coelho Dalapicola
C. F. G. Santos
SupRVLM
115
10
0
19 May 2023
Tunable Convolutions with Parametric Multi-Loss Optimization
Tunable Convolutions with Parametric Multi-Loss OptimizationComputer Vision and Pattern Recognition (CVPR), 2023
Matteo Maggioni
T. Tanay
F. Babiloni
Jingyu Sun
Alevs Leonardis
179
3
0
03 Apr 2023
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