#data augmentation
15 papers match
Teffic-Audio: Tell Fact from Fiction
Wan Lin, Li Wang, Jindong Wang +2
The paper presents Teffic-Audio, a speech deepfake detection system that uses a Conformer-based encoder with attentive pooling and a training recipe focused on multi-source data an…
Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification
Alexander Kozachok, Ilya Latyshev, Evgeny Karpulevich +3
The paper evaluates how different data augmentation strategies, especially photometric transformations and a mixed augmentation policy, can improve the out-of-domain robustness of…
Multi-Sensor Alignment for Weather Simulations
Samsad Alam, Devyani Lambhate, Aditya Mohan +2
The paper introduces methods to align weather simulations across multiple sensors for autonomous vehicle perception, including ReDAM for fog intensity and Unified-weather-edit for…
Simulating Automotive Radar with Lidar and Camera Inputs
Peili Song, Dezhen Song, Yifan Yang +2
The paper introduces a method that uses camera images, lidar point clouds, and ego-velocity to generate realistic 4D automotive radar signals via two neural networks (DIS‑Net and R…
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL
Yinggang Sun, Ziming Guo, Haining Yu +5
The paper introduces QDA-SQL, a data augmentation technique that uses large language models to generate and validate multi‑turn question‑answer pairs, improving fine‑tuned models'…
Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Zhe Liu, Huanbo Jin, Zhaohui Du +10
Pipette is an embodied simulation platform that provides open-source wet‑lab assets, a benchmark of 12 robotic tasks, and a data‑efficient augmentation pipeline to turn a few human…
Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method
Jackie Alex, Justin Petter
The paper proposes a framework that uses knowledge embedding and a hypernetwork‑guided diffusion model to generate realistic defect images of substation meters from very few annota…
Overcoming the Modality Gap in Context-Aided Forecasting
Vincent Zhihao Zheng, Ãtienne Marcotte, Arjun Ashok +4
The paper introduces a semi‑synthetic data augmentation technique to create high‑quality contextual information for time‑series forecasting, producing a 7 million‑sample dataset (C…
Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments
Mingyu Liu, Zeju Li, Jiuhe Shu +4
The paper shows that smooth robot demonstrations can miss critical alignment moments, and proposes slowing down and resampling key motion segments, plus a spatio‑temporal feature c…
When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training
Na Li, Boyu Kuang, Hongsheng Hu +4
The paper shows that mixing text‑to‑image synthetic data with real data during training can increase privacy leakage of the real samples, and introduces a method (RSMixLeak) to mea…
Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization
Adam M. Oberman
The paper provides a theoretical analysis showing that self‑supervised learning with data augmentation can achieve a fast O(1/n_L) error rate in semi‑supervised settings, linking t…
Constraint-Aware Counterfactual Editing for Aspect-Based Sentiment Analysis
S M Rafiuddin, Vamsi Krishna Pavuluri, Atriya Sen
The paper introduces CAVE-ABSA, a framework that generates and validates aspect-level counterfactual sentences for aspect‑based sentiment analysis, ensuring the target aspect’s sen…
Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification
Jeeyung Kim, Erfan Esmaeili, Qiang Qiu
The paper introduces Class-Contrastive Influence (C2I) to evaluate how useful diffusion‑generated images are for few‑shot medical classification, and uses reinforcement learning to…
MedDiffuseMix: Preserving Diagnostic Evidence with Saliency-Aware Diffusion Medical Image Data Augmentation
Teerath Kumar, Raja Vavekanand, Muhammad Turab
The paper introduces MedDiffuseMix, a saliency‑aware diffusion‑based augmentation method that mixes low‑importance regions of medical images while preserving diagnostically importa…
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan +1
The paper shows that training reinforcement‑learning agents on additional, irrelevant states acts like data augmentation and can improve zero‑shot generalization in contextual MDPs…
One search, two signals: results blend meaning (embedding similarity, so papers that never use your words still surface) with keyword matches on titles, abstracts and summaries. Free, no sign-in needed.