Methods › General › Loss Functions › Adaptive Loss

Adaptive Robust Loss

Adaptive Loss

73 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The Robust Loss is a generalization of the Cauchy/Lorentzian, Geman-McClure, Welsch/Leclerc, generalized Charbonnier, Charbonnier/pseudo-Huber/L1-L2, and L2 loss functions. By introducing robustness as a continuous parameter, the loss function allows algorithms built around robust loss minimization to be generalized, which improves performance on basic vision tasks such as registration and clustering. Interpreting the loss as the negative log of a univariate density yields a general probability distribution that includes normal and Cauchy distributions as special cases. This probabilistic interpretation enables the training of neural networks in which the robustness of the loss automatically adapts itself during training, which improves performance on learning-based tasks such as generative image synthesis and unsupervised monocular depth estimation, without requiring any manual parameter tuning.

Source: A General and Adaptive Robust Loss Function

Papers archive 2025-07-28

30 shown of 73, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 104 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Segmentation9
Semantic Segmentation8
Multi-Task Learning5
Classification4
Denoising4
Accident Anticipation3
Contrastive Learning3
Federated Learning3
General Classification3
Meta-Learning3
Object Detection3
object-detection3
regression3
Autonomous Driving2
Autonomous Vehicles2
Computed Tomography (CT)2
Depth Estimation2
Domain Adaptation2
Few-Shot Learning2
GPU2

Usage over time archive 2025-07-28

Papers per year tagged with Adaptive Loss: 2017 to 2025, peak 17 17 0 2017: 1 paper 2017 2018: 1 paper 2018 2019: 6 papers 2019 2020: 9 papers 2020 2021: 10 papers 2021 2022: 7 papers 2022 2023: 17 papers 2023 2024: 16 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (73 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Loss Functions

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections