"data augmentation" Papers
72 papers found • Page 2 of 2
Conference
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception
Yibo Wang, Ruiyuan Gao, Kai Chen et al.
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.
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood et al.
Do Generated Data Always Help Contrastive Learning?
Yifei Wang, Jizhe Zhang, Yisen Wang
DSMix: Distortion-Induced Saliency Map Based Pre-training for No-Reference Image Quality Assessment
Jinsong Shi, Jinsong Shi, Xiaojiang Peng et al.
Effective Data Augmentation With Diffusion Models
Brandon Trabucco, Kyle Doherty, Max Gurinas et al.
Emergent Equivariance in Deep Ensembles
Jan Gerken, Pan Kessel
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
Zhicai Wang, Longhui Wei, Tan Wang et al.
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective
Fangzhou Song, Bin Zhu, Yanbin Hao et al.
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification
Suorong Yang, Furao Shen, Jian Zhao
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.
Video Recognition in Portrait Mode
Mingfei Han, Linjie Yang, Xiaojie Jin et al.