Poster "data augmentation" Papers
51 papers found • Page 1 of 2
Conference
Alchemy: Amplifying Theorem-Proving Capability Through Symbolic Mutation
Shaonan Wu, Shuai Lu, Yeyun Gong et al.
Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?
Zebin You, Xinyu Zhang, Hanzhong Guo et al.
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics
Licong Lin, Song Mei
AugKD: Ingenious Augmentations Empower Knowledge Distillation for Image Super-Resolution
Yun Zhang, Wei Li, Simiao Li et al.
BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training
Xuanpu Zhang, Dan Song, pengxin zhan et al.
DataGen: Unified Synthetic Dataset Generation via Large Language Models
Yue Huang, Siyuan Wu, Chujie Gao et al.
Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Janet Wang, Yunbei Zhang, Zhengming Ding et al.
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation
Hongbin Lin, Zilu Guo, Yifan Zhang et al.
Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigation
Shohei Enomoto
HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models
Seanie Lee, Haebin Seong, Dong Bok Lee et al.
HyperMixup: Hypergraph-Augmented with Higher-order Information Mixup
Kaixuan Yao, Zhuo Li, Jianqing Liang et al.
Multi-Perspective Data Augmentation for Few-shot Object Detection
Anh-Khoa Nguyen Vu, Quoc Truong Truong, Vinh-Tiep Nguyen et al.
RouteLLM: Learning to Route LLMs from Preference Data
Isaac Ong, Amjad Almahairi, Vincent Wu et al.
RTDiff: Reverse Trajectory Synthesis via Diffusion for Offline Reinforcement Learning
Qianlan Yang, Yu-Xiong Wang
Simple, Good, Fast: Self-Supervised World Models Free of Baggage
Jan Robine, Marc Höftmann, Stefan Harmeling
Solving Instance Detection from an Open-World Perspective
Qianqian Shen, Yunhan Zhao, Nahyun Kwon et al.
TaiwanVQA: Benchmarking and Enhancing Cultural Understanding in Vision-Language Models
Hsin Yi Hsieh, Shang-Wei Liu, Chang-Chih Meng et al.
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
Maochen Yang, Zekun Li, Jian Zhang et al.
Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection
Herun Wan, Jiaying Wu, Minnan Luo et al.
Uncertainty and Influence aware Reward Model Refinement for Reinforcement Learning from Human Feedback
Zexu Sun, Yiju Guo, Yankai Lin et al.
ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis
Onkar Susladkar, Gayatri Deshmukh, Yalcin Tur et al.
What Do Latent Action Models Actually Learn?
Chuheng Zhang, Tim Pearce, Pushi Zhang et al.
Active Generation for Image Classification
Tao Huang, Jiaqi Liu, Shan You et al.
AlignDiff: Aligning Diffusion Models for General Few-Shot Segmentation
Ri-Zhao Qiu, Yu-Xiong Wang, Kris Hauser
An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains
George Eskandar
Are Synthetic Data Useful for Egocentric Hand-Object Interaction Detection?
Rosario Leonardi, Antonino Furnari, Francesco Ragusa et al.
A Simple Background Augmentation Method for Object Detection with Diffusion Model
YUHANG LI, Xin Dong, Chen Chen 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.
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation
Yididiya Nadew, Xuhui Fan, Christopher J Quinn
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation
Jun Wang, Yuzhe Qin, Kaiming Kuang et al.
Data Augmentation via Latent Diffusion for Saliency Prediction
Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang et al.
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.
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.