← Back to all news
arXiv AI By Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas, H\"usn\"u Aslan, David Balslev-Harder, Nadia A. S. Smith, Alessandra Manzin

Ctrl-A: Control-Driven Machine Learning using Adaptive Online Data Augmentation

Read the original on arXiv AI →

Ctrl-A (ControlAugment) is an automated data‑augmentation algorithm that uses control‑theory principles to adjust augmentation‑strength distributions online during training. It removes the need to pre‑set augmentation strengths by dynamically adapting each distribution based on a control‑loop and relative operation‑response curves. Experiments on CIFAR‑10, CIFAR‑100, SVHN‑core, and Tiny ImageNet with WideResNet‑28‑10 and ResNet show that Ctrl‑A achieves competitive performance across these image‑classification benchmarks.

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 AI.

Related stories

arXiv AI
Aug 20

Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets

Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets proposes LSADA, a method that constructs a learning state for each sample based on its loss and loss‑decrease rate to determine a sample‑specific augmentation strength. LSADA also introduces a decoupled data augmentation and diffusion fusion strategy that applies strength‑controlled transformations to class‑relevant regions while generating diverse class‑irrelevant regions, progressively fusing them to enhance image diversity while preserving class semantics. Experiments on nine public datasets demonstrate that LSADA outperforms the current state‑of‑the‑art dynamic GDA method by an average of 4.5% on six natural image datasets and 2.5% on three medical image datasets.

By Ting Xiang, Chenxi Deng, Jinhui Zhao, Bingting Jiang, Ke Zhang, Changjian Chen, Zhuo Tang
diffusioncomputer-visionbenchmarks
More like this →
arXiv Machine Learning
Sep 4

A Real-Calibrated Synthetic-First Data Engine

The paper introduces the Real‑Calibrated Synthetic‑First Data Engine, a modular pipeline that integrates controllable diffusion‑based synthetic image generation with multi‑stage curation, filtering, and optional uncertainty‑driven selection and human verification. Designed as a CLI‑based framework, it allows independent configuration of generation, filtering, selection, and validation modules to enhance reproducibility and flexibility in real‑world data workflows. Empirical tests on human pose estimation demonstrate that synthetic data can boost a real‑data baseline when used as low‑cost augmentation, though synthetic‑only training still lags behind real‑only performance, underscoring the importance of data‑centric orchestration in low‑data regimes.

By Yukang Shen, Zhiguo Liu, Yingshu Li, Yan Huang
agentsdiffusioncomputer-visiondata-engineering
More like this →