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.
arXiv:2608.28923v1 Announce Type: cross Abstract: Data augmentation is a cornerstone of deep learning pipelines, yet existing strategies treat it as a static, model-agnostic preprocessing step, eithe...
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.
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.
arXiv:2603. 11211v3 Announce Type: replace-cross Abstract: Incremental Learning (IL) aims to learn new tasks while preserving previously acquired knowledge.
arXiv:2607. 02612v1 Announce Type: cross Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative.
arXiv:2610.19624v1 Announce Type: new Abstract: Visual recognition models usually process a fixed view under a fixed computation budget, although images often benefit from different scales and amount...
arXiv:2407.03463v2 Announce Type: replace-cross Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets fo...
arXiv:2601. 15212v2 Announce Type: replace Abstract: Training deep computer vision models requires manual oversight or hyperparameter tuning of the learning rate (LR) schedule.
arXiv:2410.21582v4 Announce Type: replace-cross Abstract: Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of mainta...
ES‑VP introduces Energy‑Shaped Visual Prompting, a method that generates image‑specific prompts through low‑rank initialization and energy‑guided dynamic adaptation. It achieves higher performance than existing single‑prompt and diverse‑prompt approaches while using far fewer parameters. Experiments on five architectures and fifteen datasets show consistent superiority, including a 2.6% accuracy gain over DAM‑VP on CLIP with 590× fewer prompt parameters.
The paper introduces ACSAN, an All‑Convolutional Self‑Attention Network that replaces the fully‑connected matrix multiplications of Vision Transformers with convolution operations. Its core module, Dynamic Convolutional Self‑Attention (DCSA), uses multi‑scale dilated convolutions to produce Query, Key, and Value tensors, and combines spatial and channel self‑attention into a single output. Experiments on CIFAR‑10, CIFAR‑100, and ImageNet‑1k demonstrate that ACSAN attains competitive or superior classification accuracy to ViT while using comparable or fewer parameters, and ablation studies confirm the positive contribution of each component.
arXiv:2505.07675v3 Announce Type: replace-cross Abstract: Semi-supervised learning (SSL) has emerged as a practical solution for addressing data scarcity challenges by leveraging unlabeled data. Rece...