2024 "data augmentation" Papers
21 papers found
A Comprehensive Augmentation Framework for Anomaly Detection
Lin Jiang, Yaping Yan
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model
Teng Hu, Jiangning Zhang, Ran Yi et al.
Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection?
Rosario Leonardi, Antonino Furnari, Francesco Ragusa et al.
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories
Qianlan Yang, Yu-Xiong Wang
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
Yichao Cai, Yuhang Liu, Zhen Zhang et al.
Compositional Generalization for Multi-Label Text Classification: A Data-Augmentation Approach
Yuyang Chai, Zhuang Li, Jiahui Liu et al.
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation
Yididiya Nadew, Xuhui Fan, Christopher J Quinn
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation
Zelin Zang, Hao Luo, Kai Wang et al.
DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching
Guanghe Li, Yixiang Shan, Zhengbang Zhu et al.
DSMix: Distortion-Induced Saliency Map Based Pre-training for No-Reference Image Quality Assessment
Jinsong Shi, Jinsong Shi, Xiaojiang Peng et al.
Emergent Equivariance in Deep Ensembles
Jan Gerken, Pan Kessel
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho et al.
First-Order Manifold Data Augmentation for Regression Learning
Ilya Kaufman, Omri Azencot
Image-Feature Weak-to-Strong Consistency: An Enhanced Paradigm for Semi-Supervised Learning
Zhiyu Wu, Jin shi Cui
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David Zhang et al.
LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training
Khoi M. Le, Trinh Pham, Tho Quan et al.
Sample-Efficient Multiagent Reinforcement Learning with Reset Replay
Yaodong Yang, Guangyong Chen, Jianye Hao et al.
Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
Damien Teney, Jindong Wang, Ehsan Abbasnejad
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Chi-Heng Lin, Chiraag Kaushik, Eva Dyer et al.
TiMix: Text-Aware Image Mixing for Effective Vision-Language Pre-training
Chaoya Jiang, Wei Ye, Haiyang Xu et al.
Towards Improved Proxy-Based Deep Metric Learning via Data-Augmented Domain Adaptation
Li Ren, Chen Chen, Liqiang Wang et al.