How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization

12 October 2022

Papers citing "How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization"

31 / 31 papers shown

Title
Narrowing Class-Wise Robustness Gaps in Adversarial Training Fatemeh Amerehi Patrick Healy 35 0 0 20 Mar 2025
Elliptic Loss Regularization Ali Hasan Haoming Yang Yuting Ng Vahid Tarokh 58 1 0 04 Mar 2025
(Mis)Fitting: A Survey of Scaling Laws Margaret Li Sneha Kudugunta Luke Zettlemoyer 69 2 0 26 Feb 2025
Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data Sreyan Ghosh Sonal Kumar Zhifeng Kong Rafael Valle Bryan Catanzaro Dinesh Manocha DiffM 39 2 0 02 Oct 2024
Understanding the Role of Invariance in Transfer Learning Till Speicher Vedant Nanda Krishna P. Gummadi SSL OOD 31 1 0 05 Jul 2024
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions Sreyan Ghosh Utkarsh Tyagi Sonal Kumar C. K. Evuru S Ramaneswaran S. Sakshi Dinesh Manocha 31 5 0 06 Jun 2024
Measuring Style Similarity in Diffusion Models Gowthami Somepalli Anubhav Gupta Kamal Gupta Shramay Palta Micah Goldblum Jonas Geiping Abhinav Shrivastava Tom Goldstein EGVM 38 35 0 01 Apr 2024
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP Chandra Kiran Reddy Evuru Sreyan Ghosh Sonal Kumar S. Ramaneswaran Utkarsh Tyagi Dinesh Manocha 32 3 0 30 Mar 2024
Perspectives on the State and Future of Deep Learning - 2023 Micah Goldblum A. Anandkumar Richard Baraniuk Tom Goldstein Kyunghyun Cho Zachary Chase Lipton Melanie Mitchell Preetum Nakkiran Max Welling Andrew Gordon Wilson 46 3 0 07 Dec 2023
Understanding the Detrimental Class-level Effects of Data Augmentation Polina Kirichenko Mark Ibrahim Randall Balestriero Diane Bouchacourt Ramakrishna Vedantam Hamed Firooz Andrew Gordon Wilson 27 12 0 07 Dec 2023
Simplifying Neural Network Training Under Class Imbalance Ravid Shwartz-Ziv Micah Goldblum Yucen Lily Li C. B. Bruss Andrew Gordon Wilson 15 14 0 05 Dec 2023
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios Swarna Kamlam Ravindran Carlo Tomasi 19 0 0 25 Nov 2023
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images Logan Frank Jim Davis 20 0 0 20 Oct 2023
Improve Deep Forest with Learnable Layerwise Augmentation Policy Schedule Hongyu Zhu Sichu Liang Wentao Hu Fangqi Li Yali Yuan Shi-Lin Wang Guang Cheng 8 2 0 16 Sep 2023
Using and Abusing Equivariance Tom Edixhoven A. Lengyel J. C. V. Gemert 11 3 0 22 Aug 2023
A Holistic Assessment of the Reliability of Machine Learning Systems Anthony Corso David Karamadian Romeo Valentin Mary Cooper Mykel J. Kochenderfer 13 6 0 20 Jul 2023
Layer-wise Linear Mode Connectivity Linara Adilova Maksym Andriushchenko Michael Kamp Asja Fischer Martin Jaggi FedML FAtt MoMe 26 15 0 13 Jul 2023
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images Mokshagna Sai Teja Karanam Tushar Kataria Krithika S. Iyer Shireen Elhabian MedIm 11 3 0 06 Jul 2023
Variance-Covariance Regularization Improves Representation Learning Jiachen Zhu Katrina Evtimova Yubei Chen Ravid Shwartz-Ziv Yann LeCun SSL 10 7 0 23 Jun 2023
Subspace-Configurable Networks Dong Wang O. Saukh Xiaoxi He Lothar Thiele OOD 22 0 0 22 May 2023
To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review Ravid Shwartz-Ziv Yann LeCun SSL 6 71 0 19 Apr 2023
Fake it till you make it: Learning transferable representations from synthetic ImageNet clones Mert Bulent Sariyildiz Alahari Karteek Diane Larlus Yannis Kalantidis DiffM VLM 15 152 0 16 Dec 2022
Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning Zenglin Shi Jing Jie Ying Sun J. Lim Mengmi Zhang CLL 29 1 0 21 Nov 2022
tf.data service: A Case for Disaggregating ML Input Data Processing Andrew Audibert Yangrui Chen D. Graur Ana Klimovic Jiří Šimša C. A. Thekkath 31 16 0 26 Oct 2022
A Simple Strategy to Provable Invariance via Orbit Mapping Kanchana Vaishnavi Gandikota Jonas Geiping Zorah Lähner Adam Czapliñski Michael Moeller AAML 3DPC 13 3 0 24 Sep 2022
Patches Are All You Need? Asher Trockman J. Zico Kolter ViT 214 395 0 24 Jan 2022
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 198 477 0 01 Oct 2021
Stochastic Training is Not Necessary for Generalization Jonas Geiping Micah Goldblum Phillip E. Pope Michael Moeller Tom Goldstein 81 72 0 29 Sep 2021
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 210 1,391 0 04 Dec 2018
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 279 39,083 0 01 Sep 2014
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 160 25,150 0 09 Jun 2011