Study examines how data augmentation impacts optimization in linear regression.
problem Understanding how data augmentation schedules affect optimization in linear regression.
method Analyzed the effect of augmentation on optimization in linear regression with MSE loss, using classical convex optimization and recent work on implicit bias.
result Proved that under certain joint schedules for learning rate and augmentation scheme, augmented gradient descent converges and characterized the resulting minimum.
Data augmentation doesn't improve robustness, contrary to belief.
problem The effectiveness of data augmentation in improving model robustness is questioned.
method Taking a Domain Generalization viewpoint, the study examines the robustness of augmented representations.
result Augmented representations are not robust to distortions used during training.
This paper improves auto-augment efficiency by sharing augmentation weights.
problem Efficient evaluation of augmentation policies for model training.
method Augmentation-Wise Weight Sharing (AWS) to create a fast yet accurate proxy task.
result Augmentation policies found achieve superior accuracies compared to existing methods.
WeMix improves data augmentation by correcting bias in deep learning.
problem Data augmentation's effectiveness is limited by data bias.
method Developed AugDrop and MixLoss algorithms to correct data bias.
result WeMix improves data augmentation performance through bias correction.
A study on optimizing data augmentation weights for improved test-time predictions.
problem Improving robustness of predictions during testing with data augmentation methods.
method A weighted Test-Time Augmentation (TTA) approach based on variational Bayesian framework to optimize weights.
result Optimizing weights suppresses unwanted data augmentations and improves prediction performance.
Data augmentation can achieve the same statistical benefits as full augmentation up to an approximation error.
problem Data augmentation in learning problems
method Using Fourier analysis and representation theory of finite groups
result Partial data augmentation achieves the same minimax rates as full augmentation
Study fully augmented links in thickened torus, generalizing S3 results.
problem Classify and describe geometric properties of fully augmented links in thickened torus.
method Geometric analysis and decomposition of link complements into ideal right-angled torihedra.
result Proves Volume Density Conjecture for fully augmented links in thickened torus.
Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks. Recently, researchers proposed a few intensive data augmentation techniques, which indeed improved accuracy, yet we notice that these methods augment data have also caused a …
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. Properly chosen augmentation policies can lead to significant generalization improvements; however, state-of-the-art approaches such as AutoAugment …
SapAugment learns adaptive augmentation policies for better model training.
problem Fixed data augmentation methods often apply the same augmentation to all samples, ignoring sample difficulty.
method SapAugment adapts augmentation parameters based on training loss, learning a sample-adaptive policy.
result SapAugment achieves up to 21% relative reduction in word error rate on LibriSpeech dataset.
Automatically learns optimal data augmentation for image classification.
problem Finding optimal data augmentation hyperparameters is computationally demanding and requires domain knowledge.
method Proposes an online bilevel optimization framework to learn data augmentation parameters directly.
result Jointly trained method achieves comparable or better classification accuracy than hand-crafted data augmentation without an external validation loop.
Data augmentations are important ingredients in the recipe for training robust neural networks, especially in computer vision. A fundamental question is whether neural network features encode data augmentation transformations. To answer this question, we introduce a systematic approach to investigate which layers of ne…
Graph data augmentation improves GNN performance in node classification.
problem Improving generalizability of graph neural networks (GNNs) in semi-supervised node classification.
method Introduces GAug framework for graph data augmentation using neural edge predictors.
result GAug framework improves GNN-based node classification performance across various architectures and datasets.
This work investigates image augmentations for GAN training, improving image quality.
problem Improving the accuracy and robustness of GAN models for image synthesis.
method Systematic study of various image augmentation techniques for GAN training.
result Vanilla GANs can achieve state-of-the-art generation quality with image augmentations.
A method to prevent image representation collapse through data-dependent augmentation.
problem Representation collapse due to image augmentations that damage information.
method Formalizing a stochastic encoding process with a tug-of-war between corruption and preserved information, using infoMax objective.
result Learning a data-dependent distribution of augmentations to avoid representation collapse.
We describe a correspondence between augmentations and certain representations of the knot group. The correspondence makes the 2-variable augmentation polynomial into a generalization of the classical A-polynomial. It also associates to an augmentation a rank, which is bounded by the bridge number and shares its beha…
New attacks show data augmentation may not improve privacy.
problem Measuring privacy risk in models trained with data augmentation.
method Formulated membership inference as a set classification problem, designed input permutation invariant features.
result Proposed approach universally outperforms original methods on models trained with data augmentation.
Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of either distribution shift or augmentation diversity. Inspired…
Expert augmentation improves hybrid model generalization.
problem Limited generalization of hybrid models outside training distribution.
method Introducing expert augmentation to improve hybrid model performance.
result Expert augmentation improves generalization of hybrid models.
Augmentation improves machine learning model performance on small datasets.
problem Suboptimal generalization performance of machine learning models on small datasets.
method Data augmentation to increase sample size and diversity.
result Augmentation improves AUC by 15.55% on average for small datasets.
This work characterizes how data augmentation shapes neural representations.
problem Understanding the impact of data augmentation on neural network representations.
method Embedding neural network hidden representations into a metric space invariant to transformations, analyzing shape-space trajectories.
result Increasing data augmentation strength leads to well-behaved trajectories in the embedded space, and different augmentation types steer representations in distinct directions.
Test-time data augmentation−averaging the predictions of a machine learning model across multiple augmented samples of data−is a widely used technique that improves the predictive performance. While many advanced learnable data augmentation techniques have emerged in recent years, they are focused on the training p…
ADA augments data using AR replicas for robust regression.
problem Improving robustness in nonlinear over-parametrized regression.
method Extends Anchor regression (AR) for data augmentation, using replicas of modified samples.
result ADA provides more robust regression predictions compared to state-of-the-art solutions.
We construct the augmentation representation. It is a representation of the fundamental group of the link complement associated to an augmentation of the framed cord algebra. This construction connects representations of two link invariants of different types. We also study properties of the augmentation representation…
Counterfactual data augmentations may not ensure OOD robustness if performed by a context-guessing machine.
problem Deep learning models lack out-of-distribution robustness due to reliance on spurious features.
method Theoretical analysis and demonstration of counterfactual data augmentations performed by a context-guessing machine.
result Counterfactual data augmentations by a context-guessing machine do not lead to robust OOD classifiers.
Transfer learning and data augmentation improve stock classification performance.
problem Challenges in stock classification due to noise and volatility.
method Pre-trained model on S&P500 index features, transfer learning to new models, data augmentation on feature space.
result Augmentation on feature space leads to 20% increase in risk-adjusted returns.
Develops a statistical framework for self-supervised representation learning using data augmentation.
problem Lack of theoretical understanding of data augmentation in nonlinear settings.
method Augmentation invariant manifold learning framework and stochastic optimization algorithm.
result Improves downstream analysis by exploiting manifold's geometric structure and invariant property of augmented data.
Data augmentation impacts adversarial risk; careful application recommended.
problem Understanding how data augmentation affects adversarial risk in deep learning.
method Empirical analysis using three measures of adversarial risk.
result Data augmentation does not always improve adversarial risk; augmented data influences models more.
Fully augmented links have dense volume densities but discrete in certain ranges.
problem Characterizing the volume density spectrum of fully augmented links.
method Analyzing the ratio of volume to the number of augmentations.
result The set of FAL volume densities is dense in $[2\voct, 10\vtet)$ but discrete in $[\voct,2\voct)$.
Data augmentation affects feature importance, enhancing learning for neural networks.
problem Understanding the effect of data augmentation on feature importance and learning dynamics.
method Analyzing a two-layer convolutional neural network in a multi-view data model.
result Data augmentation alters feature importance, making certain features more likely to be learned.
A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional met…
Self-training with noisy student-teacher boosts keyword spotting accuracy.
problem Robust keyword spotting in challenging conditions.
method Aggressive data augmentation and self-training with noisy student-teacher approach.
result Significant accuracy improvement in difficult conditions, up to 60%.
ODVICE augments EHR cohorts using ontology to improve analysis robustness.
problem Limited records in cohorts for rare diseases hamper robust analysis.
method Ontology-driven Monte-Carlo graph spanning algorithm for data augmentation.
result ODVICE augmented cohorts show ~30% improvement in AUC over non-augmented datasets.
Flat fully augmented links with homeomorphic complements are equivalent.
problem Determining equivalence of flat fully augmented links based on their complements.
method Careful analysis of totally geodesic surfaces and cusps in link complements and their behavior under homeomorphism.
result Complete classification of flat fully augmented link complements with multiple reflection surfaces and symmetries.
This work analyzes the role of data augmentation in self-supervised learning using RKHS approximation and regression.
problem Limited theoretical understanding of the role of data augmentation in self-supervised learning.
method Geometric characterization of the target function given by augmentation, proving generalization bounds.
result Two generalization bounds are derived, one free of model complexity, the other specific to near-optimal encoders.
Survey of data augmentation techniques for time series classification with neural networks.
problem Small datasets in time series recognition.
method Four families of data augmentation: transformation-based, pattern mixing, generative models, and decomposition methods.
result Empirical evaluation of 12 data augmentation methods on 128 datasets.
Data augmentation improves microbiome disease prediction.
problem Improving predictive models for microbiome data.
method Defined novel data augmentation strategies for simplex-valued data.
result Set new state-of-the-art for disease prediction tasks.
The paper constructs Yang-Baxter solutions using categorical augmented racks.
problem Solutions to the Yang-Baxter equation in knot theory.
method Interpreting augmented racks in tensor categories and constructing solutions using quantum heaps and Hopf algebra modules.
result Explicit constructions and infinite families of Yang-Baxter solutions are provided.
Generalizes fully augmented links to doubled 3-manifolds with geometric bounds.
problem Understanding the geometry of fully augmented links in doubled 3-manifolds.
method Constructing fully augmented links on the reflection surface of doubled 3-manifolds and finding bounds on cusp shapes and volumes.
result Bounds on cusp shapes and volumes of hyperbolic links in doubled 3-manifolds.
Bayesian neural networks with data augmentation show a persistent cold posterior effect.
problem Understanding the cold posterior effect in Bayesian neural networks with data augmentation.
method Developed principled Bayesian neural networks using data augmentation, providing exact likelihoods and tight bounds.
result The cold posterior effect persists even in models incorporating data augmentation, suggesting it's not an artifact.
The paper explains how data augmentation improves semi-supervised learning efficiency.
problem Improving accuracy from a small fraction of labeled data.
method Data augmentation induces a similarity graph, which is graph-Laplacian-regularized for downstream learning.
result A fast transductive rate of O(1/nL) is achieved, reducing the number of labels needed. The study explores Legendrian fillings and augmentations, providing methods to compute induced augmentations.
problem Understanding and computing Legendrian isotopy invariants through augmentations and fillings.
method Developed methods to compute induced augmentations based on Morse complex families and Legendrian cobordisms.
result Established methods to compute Legendrian isotopy invariants using augmentations and fillings.
New augmentations of twist knots found that can't be filled.
problem Finding augmentations of twist knots that cannot be filled by orientable Lagrangian fillings.
method Using a Floer-theoretic version of a result from microlocal sheaf theory, showing augmentations cannot be induced by algebraic tori.
result Established new examples of augmentations of Legendrian twist knots that cannot be induced by orientable Lagrangian fillings.
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
The paper shows how data augmentation and regularization can enforce group equivariance in machine learning models.
problem Improving model performance by leveraging known symmetries in machine learning tasks.
method Training with data augmentation and regularization to enforce group equivariance.
result Equivariance of the trained model can be achieved through training on augmented data in tandem with regularization.
SODA optimizes data augmentation allocation for deep learning models.
problem Inefficient allocation of data augmentation budget in deep neural networks.
method Online learning to dynamically allocate data augmentation budget during training.
result Optimized data augmentation can save computation time and promote greener machine learning.
Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines. In this paper, we seek to establish a theoretical framework for understanding data augmentation. We approach this from two directions: First, we provide a general…
Data augmentation improves financial prediction models, especially for small datasets.
problem Improving financial prediction models on small, noisy, non-stationary datasets.
method Evaluation of data augmentation methods combined with deep learning models on financial datasets.
result Data augmentation significantly improves financial performance, up to 400% improvement in risk-adjusted return.