Image Augmentation
100 papers with code • 1 benchmarks • 1 datasets
Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.
Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing
( Image credit: Kornia )
Benchmarks
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Libraries
Use these libraries to find Image Augmentation models and implementations
Intel Image Classification
Latest papers with no code
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining
Contrastive Language-Image Pretraining has emerged as a prominent approach for training vision and text encoders with uncurated image-text pairs from the web.
Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents.
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies
High-resolution fMRI provides a window into the brain's mesoscale organization.
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
We first uncover the core principle of gradient inversion that enables these attacks and theoretically identify the main conditions by which the defense can be robust regardless of the attack strategies.
Geometric Data Augmentations to Mitigate Distribution Shifts in Pollen Classification from Microscopic Images
We leverage the domain knowledge that geometric features are highly important for accurate pollen identification and introduce two novel geometric image augmentation techniques to significantly narrow the accuracy gap between the model performance on the train and test datasets.
Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques
Our results show that the proposed methodology leads to segmentation models that are more resilient to variations in histology slides whilst maintaining high levels of segmentation performance, and show improved segmentation of the tumour class when compared to previous research.
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot
This work specifically tackles the first challenge by proposing a novel Digital-Twin(DT)MARS-CycleGAN model for image augmentation to improve our Modular Agricultural Robotic System (MARS)'s crop object detection from complex and variable backgrounds.
GPT-Prompt Controlled Diffusion for Weakly-Supervised Semantic Segmentation
In this process, the existing images and image-level labels provide the necessary control information, where GPT is employed to enrich the prompts, leading to the generation of diverse backgrounds.
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning
The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of symbol-like entities.
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix
Fitness applications are commonly used to monitor activities within the gym, but they often fail to automatically track indoor activities inside the gym.
