arXiv subjects

Ying Kiat Tan

Publications and source records attributed to Ying Kiat Tan.

3 recordsLinked to original sources

arXiv2026

Can One-Shot Test-Time Data Augmentation Help with Generalization?

Data augmentation is crucial for model generalization, but existing methods are mostly centered on the training stage. Test-time augmentation, while underexplored, can be practically effective for generalization while avoiding extra model parameters or fine-tuning. Given the increasing training cost and the literature gap, we study whether it is possible to perform effective test-time augmentation using image generation from just the single original image. We first analyze the importance of test-time augmentation, and then design and study a simple yet natural operator named 1S-DAug, which comprises geometric perturbations with controlled noise injection and image-conditioned denoising. We obtain positive results on well-established image-classification benchmarks across four datasets and multiple models, achieving up to 20\% relative accuracy improvement without model training or parameter access. Code will be released.

cs.CV↗

arXiv2026

Can We Change the Stroke Size for Easier Diffusion?

Diffusion models can be challenged in the low signal-to-noise regime, where they have to make pixel-level predictions despite the presence of high noise. The geometric intuition is akin to using the finest stroke for oil painting throughout, which may be ineffective. We therefore study \emph{stroke-size control} as a controlled intervention that changes the roughness of the supervised target, predictions and perturbations across timesteps, in an attempt to ease the low signal-to-noise challenge via the prediction target simplification.

cs.CV↗

arXiv2024

FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation

Few-shot learning (FSL) commonly requires a model to identify images (queries) that belong to classes unseen during training, based on a few labelled samples of the new classes (support set) as reference. So far, plenty of algorithms involve training data augmentation to improve the generalization capability of FSL models, but outlier queries or support images during inference can still pose great generalization challenges. In this work, to reduce the bias caused by the outlier samples, we generate additional test-class samples by combining original samples with suitable train-class samples via a generative image combiner. Then, we obtain averaged features via an augmentor, which leads to more typical representations through the averaging. We experimentally and theoretically demonstrate the effectiveness of our method, obtaining a test accuracy improvement proportion of around 10\% (e.g., from 46.86\% to 53.28\%) for trained FSL models. Importantly, given a pretrained image combiner, our method is training-free for off-the-shelf FSL models, whose performance can be improved without extra datasets nor further training of the models themselves. Codes are available at https://github.com/WendyBaiYunwei/FSL-Rectifier-Pub.

cs.CV↗