Image Augmentation
98 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
XoFTR: Cross-modal Feature Matching Transformer
We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images.
Evolving Loss Functions for Specific Image Augmentation Techniques
We exploit this disparity by performing an evolutionary search on five types of image augmentation techniques in the hopes of finding image augmentation specific loss functions.
Genetic Learning for Designing Sim-to-Real Data Augmentations
This is because they widen the training data distribution, thus encouraging the model to generalize better to other domains.
DiffClass: Diffusion-Based Class Incremental Learning
On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data.
Outline-Guided Object Inpainting with Diffusion Models
We achieve that by creating variations of the available annotated object instances in a way that preserves the provided mask annotations, thereby resulting in new image-mask pairs to be added to the set of annotated images.
Fiducial Focus Augmentation for Facial Landmark Detection
To effectively utilize the newly proposed augmentation technique, we employ a Siamese architecture-based training mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss to achieve collective learning of high-level feature representations from two different views of the input images.
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition
Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech.
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks
We propose a new search space for NLFS that encourages more diverse loss functions to be explored, and a surrogate function that accurately transfers to large-scale convolutional neural networks.
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN
Deep learning has made significant advances in computer vision, particularly in image classification tasks.
Leveraging Habitat Information for Fine-grained Bird Identification
Training CNNs and ViTs with habitat-augmented data results in an improvement of up to +0. 83 and +0. 23 points on NABirds and CUB-200, respectively.
