← Back to all news
arXiv Computer Vision By Jaewan Ko, Janghoon Choi

DLC: A Metric-Guided Dynamic Loss Controller for Multi-Objective Training

Read the original on arXiv Computer Vision →

The paper introduces DLC, a metric-guided dynamic loss controller that adjusts the weighting of existing training losses during multi-objective image restoration. DLC evaluates the model on a fixed feedback subset and uses quality metrics to update the loss-weight vector via an LLM-based controller, without altering the restoration architecture or adding new loss terms. Experiments on low-light enhancement, deraining, and super-resolution demonstrate that DLC guides training toward balanced fidelity and perceptual quality across diverse domains.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

Hugging Face Trending Papers
Jul 30

CoRE-UIR: Prior-guided common and residual experts for efficient all-in-one remote sensing image restoration

Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imaging artifacts, which may co-occur and jointly induce global distribution shifts and local structural corruption. Although All-in-One image restoration offers an appealing unified alternative to task-specific pipelines, existing methods still suffer from weak or implicit degradation cues and parameter redundancy caused by full-rank multi-expert designs with overlapping restoration behaviors.

rag
More like this →