Data Augmentation
2517 papers with code • 2 benchmarks • 63 datasets
Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.
Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.
Further readings:
- A Survey of Data Augmentation Approaches for NLP
- A survey on Image Data Augmentation for Deep Learning
( Image credit: Albumentations )
Benchmarks
These leaderboards are used to track progress in Data Augmentation
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Libraries
Use these libraries to find Data Augmentation models and implementations
CIFAR-10
ImageNet
ESC-50
ImageNet-Sketch
MuST-C
IAM
PAWS
Clotho
UrbanSound8K
MathQA
Latest papers with no code
MambaPupil: Bidirectional Selective Recurrent model for Event-based Eye tracking
Event-based eye tracking has shown great promise with the high temporal resolution and low redundancy provided by the event camera.
Guided Discrete Diffusion for Electronic Health Record Generation
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e. g., disease progression prediction, clinical trial design, and health economics and outcomes research.
Revisiting Noise Resilience Strategies in Gesture Recognition: Short-Term Enhancement in Surface Electromyographic Signal Analysis
We propose a Short Term Enhancement Module(STEM) which can be easily integrated with various models.
Achieving Rotation Invariance in Convolution Operations: Shifting from Data-Driven to Mechanism-Assured
Based on various types of non-learnable operators, including gradient, sort, local binary pattern, maximum, etc., this paper designs a set of new convolution operations that are natually invariant to arbitrary rotations.
Simple In-place Data Augmentation for Surveillance Object Detection
Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications.
D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes
To address this issue, we propose D-Aug, a LiDAR data augmentation method tailored for augmenting dynamic scenes.
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains.
Continuous Control Reinforcement Learning: Distributed Distributional DrQ Algorithms
Distributed Distributional DrQ is a model-free and off-policy RL algorithm for continuous control tasks based on the state and observation of the agent, which is an actor-critic method with the data-augmentation and the distributional perspective of critic value function.
Offline Trajectory Generalization for Offline Reinforcement Learning
Then we propose four strategies to use World Transformers to generate high-rewarded trajectory simulation by perturbing the offline data.
Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis
Recently, growing health awareness, novel methods allow individuals to monitor sleep at home.
