HTC implementation using PyTorch (supports MOSAIC/MixUp and RandomAugment)
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Updated
Mar 31, 2022 - Python
HTC implementation using PyTorch (supports MOSAIC/MixUp and RandomAugment)
A repository to host recent papers on Manifold Mixup.
Python package for data augmentation inspired by Mixup: Beyond Empirical Risk Minimization
CascadeRCNN implementation using PyTorch
Tensorflow2/KerasのImageDataGenerator向けのmixupの実装。
Bronze medal solution for the "Bengali.AI Handwritten Grapheme Classification" Kaggle competition
Code for the ACL 2023 long paper "Composition-contrastive Learning for Sentence Embeddings"
DualDet implementation using PyTorch
This repo implements a ViT based model with Mixup Data Augmentation method. All the models including ViT are implemented from scratch using tensorflow
6-th task solution of DCASE2020
Python codes to implement DeMix, a DETR assisted CutMix method for image data augmentation
About Official PyTorch(MMCV) implementation of “SUMix: Mixup with Semantic and Uncertain Information” (ECCV 2024)
HTCLite implementation using PyTorch (supports MOSAIC/MixUp and RandomAugment)
Classification using Vision Transformers (ViT) and MixUp Augmentation
Model Compression using Knowledge Distillation
An R package inspired by 'mixup: Beyond Empirical Risk Minimization'
To evaluate the performance of each regularization method (cutout, mixup, and self-supervised rotation predictor), we apply it to the CIFAR-10 dataset using a deep residual network with a depth of 20 (ResNet20)
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