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FlowLet is a conditional generative framework that synthesizes age-conditioned 3D brain MRIs using flow matching in an invertible wavelet domain, improving brain age prediction accuracy for underrepresented age groups with high efficiency.
This paper presents WISE-HAR, an ensemble deep learning framework for WiFi-based human activity recognition, achieving robust performance and generalization across scenarios with minimal accuracy drops.
GLENS is a data-efficient global search method that uses diffusion models to generate diverse, high-quality initial guesses for local minima in non-convex optimization problems by leveraging intermediate solver iterates as free data augmentation.
This exploratory study empirically measures the symmetry–data exchange rate predicted by equivariance theory on controlled C_n-symmetric tasks, finding that wrong-group constraints are actively harmful, augmentation with test-time orbit averaging matches equivariant models exactly, and the empirical exchange rate is broadly consistent with theory but statistically inconclusive. The authors emphasize the study's exploratory nature and call for registered replications.
Proposes SSDAU, a structured semantic data augmentation method for joint entity and relation extraction that preserves semantic structure by segmenting text based on entity labels and using BERTTopic for topic consistency, significantly outperforming existing augmentation methods.
TERGAD is a novel data augmentation framework that uses large language models to translate node-level topological properties into semantic narratives, then fuses these with original node attributes via a gated dual-branch autoencoder for graph anomaly detection, achieving state-of-the-art results on six datasets.
This paper uses shape analysis tools to characterize how different data augmentation strategies reshape the geometry of neural network representations, finding that augmentation strength and type lead to distinct, well-behaved trajectories in shape space.
This paper proposes a large language model-driven data augmentation framework using GPT-5 to generate synthetic oral monologues from written anchors for cognitive score prediction from speech. A similarity-guided selection strategy consistently reduces prediction error, particularly for minority low-score participants.
This paper proposes a context-aware synthetic augmentation framework combined with a hybrid classification model to address data scarcity and class imbalance in classifying psychological defense mechanisms from text. The method achieves significant improvements on the PsyDefDetect shared task benchmark.
The article introduces DataArc-SynData-Toolkit, an open-source framework designed to simplify multi-path, multimodal, and multilingual synthetic data generation. It aims to lower technical barriers and improve usability for training large language models through a unified, configuration-driven pipeline.
This paper introduces GSM-SEM, a framework for generating semantically diverse benchmark variants to mitigate memorization in mathematical reasoning evaluations. The authors demonstrate that this approach reveals significant performance drops in current SOTA LLMs compared to static benchmarks.
Proposes TAP, a tabular augmentation policy that couples diffusion inpainting with a learner-conditioned policy to improve downstream model performance under data scarcity, outperforming strong baselines on real-world datasets.
This paper investigates how informal text (slang, emoji, Gen-Z filler tokens) degrades NLI accuracy in ELECTRA-small and RoBERTa-large models, identifying two distinct failure mechanisms—tokenization failure (emoji mapped to [UNK]) and distribution shift (out-of-domain noise tokens)—and proposes targeted mitigations that recover accuracy without harming clean-text performance.
OpenAI presents a simple data augmentation technique that enables autoregressive language models to perform fill-in-the-middle (FIM) text generation without harming left-to-right performance, with extensive ablations and best practices provided for training such models.