Popular GitHub Repositories for Stronger Augmentations

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RAD: Reinforcement Learning with Augmented Data

Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)

Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets) NeurIPS'24

[NeurIPS 2024 Oral?] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.

Implementation of PALI3 from the paper PALI-3 VISION LANGUAGE MODELS: SMALLER, FASTER, STRONGER"

Train Scene Graph Generation for Visual Genome and GQA in PyTorch >= 1.2 with improved zero and few-shot generalization.

[NeurIPS 2023] FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

[CVPR 2022] "Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations" by Tianlong Chen*, Peihao Wang*, Zhiwen Fan, Zhangyang Wang

[ICLR 2023] Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners

[ICML 2021] "Graph Contrastive Learning Automated" by Yuning You, Tianlong Chen, Yang Shen, Zhangyang Wang; [WSDM 2022] "Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen

Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut, ICML 2022.

official implemntation for "Contrastive Learning with Stronger Augmentations"

[ECCV 2024] Official Repo for: Dataset Enhancement with Instance-Level Augmentations

[KDD'22] Source codes of "Graph Rationalization with Environment-based Augmentations"

[NeurIPS 2020 Spotlight Oral] "Training Stronger Baselines for Learning to Optimize", Tianlong Chen*, Weiyi Zhang*, Jingyang Zhou, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang

[NeurIPS 2021] “Stronger NAS with Weaker Predictors“, Junru Wu, Xiyang Dai, Dongdong Chen, Yinpeng Chen, Mengchen Liu, Ye Yu, Zhangyang Wang, Zicheng Liu, Mei Chen and Lu Yuan

In this work I investigate the speech command task developing and analyzing deep learning models. The state of the art technology uses convolutional neural networks (CNN) because of their intrinsic nature of learning correlated represen- tations as is the speech. In particular I develop different CNNs trained on the Google Speech Command Dataset and tested on different scenarios. A main problem on speech recognition consists in the differences on pronunciations of words among different people: one way of building an invariant model to variability is to augment the dataset perturbing the input. In this work I study two kind of augmentations: the Vocal Tract Length Perturbation (VTLP) and the Synchronous Overlap and Add (SOLA) that locally perturb the input in frequency and time respectively. The models trained on augmented data outperforms

RuTransform: python framework for adversarial attacks and text data augmentation for Russian

A PyTorch implementation of "Self-Supervised GNN that Jointly Learns to Augment" or "Jointly Learnable Data Augmentations for Self-Supervised GNNs" papers, which appeared at Neurips2021 Workshop on Self-Supervised Learning - Theory and Practice