Improves fairness in machine learning by adding underrepresented group data.
problem Machine learning biases across subgroups due to under-representation or societal biases.
method Data augmentation via pairwise mixup across subgroups to balance subpopulations.
result Achieves fair outcomes with robust if not improved accuracy.
Mixup improves model robustness and generalization by convexly combining examples.
problem Improving model robustness and generalization.
method Using Mixup augmentation in training, which involves convex combinations of pairs of examples and their labels.
result Mixup training helps models exhibit robustness to adversarial attacks and reduces overfitting.
Paper examines fairness of data augmentation methods, finding vanilla Mixup outperforms Fair Mixup.
problem Improving fairness in classification models with limited minority data.
method Uses multicalibration to rigorously evaluate and improve data augmentation methods for classification fairness.
result Vanilla Mixup outperforms Fair Mixup and baseline methods in fairness and accuracy, especially with small minority groups.
Fair Mixup improves fairness in classifiers by interpolating between groups.
problem Ensuring fairness in classifiers during training and evaluation.
method Fair Mixup uses interpolation of samples between groups to enforce fairness constraints.
result Fair Mixup ensures better generalization of fairness in various benchmarks.
Mixup reduces the sample complexity of finding optimal decision boundaries for more separable data.
problem Finding optimal decision boundaries in separable data distributions.
method Mixup technique applied to binary linear classification problems.
result Mixup significantly reduces the sample complexity for more separable data.
Noisy Feature Mixup improves model robustness with noise-perturbed convex combinations.
problem Improving model robustness against data perturbations.
method Noise-perturbed convex combinations of pairs of data points in input and feature space.
result Improved model robustness and favorable trade-offs between accuracy and robustness.
Mixes higher-order simplicial complexes for data augmentation.
problem Lack of labeled data for complex systems with multiway interactions.
method Proposes mixup mechanisms for simplicial complexes, including linear and nonlinear mixup, and a convex clustering mixup.
result Synthetic simplicial complexes interpolate between existing data based on homomorphism densities.
Mixup improves feature learning by mixing common and rare features.
problem Improving generalization in deep learning models.
method Mixup, a data augmentation technique, is applied to feature learning. Theoretical and experimental studies are conducted to understand its benefits.
result Mixup effectively learns rare features from common ones, leading to better generalization.
Mixup technique improved, reducing manifold mismatch for better calibration.
problem Improving calibration of models using Mixup.
method Dynamic adjustment of interpolation coefficients based on sample similarity.
result Improved predictive performance and calibration with reduced manifold mismatch.
Enhances neural network robustness with Mixup and TLAT.
problem Neural networks are sensitive to various perturbations and adversarial examples.
method Combines Mixup augmentation with Targeted Labeling Adversarial Training (TLAT).
result M-TLAT increases robustness against 19 corruptions and 5 adversarial attacks without reducing clean sample accuracy.
Data augmentation by mixing samples, such as Mixup, has widely been used typically for classification tasks. However, this strategy is not always effective due to the gap between augmented samples for training and original samples for testing. This gap may prevent a classifier from learning the optimal decision boundar…
Mixup improves model performance by interpolating random training examples.
problem Overfitting in machine learning models.
method Mixup is a regularization procedure that linearly interpolates random pairs of training examples.
result Mixup works well from a statistical learning theory perspective.
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.
A new method MixGDA combines mixup and gradient-based data augmentation for SSL.
problem Improving semi-supervised learning performance with limited labeled data.
method Gradient-based Data Augmentation (GDA) combined with mixup methods.
result MixGDA achieves state-of-the-art performance in various SSL benchmarks.
Mixup improves model accuracy and calibration through data transformation and random perturbation.
problem Improving model accuracy and calibration in machine learning.
method Interprets Mixup as empirical risk minimization with data transformation and random perturbation.
result Mixup induces multiple known regularization schemes that prevent overfitting and overconfident predictions.
Enhances machine learning models by preserving data structure, addressing statistical distortions.
problem Statistical distortions in synthetic data generated by Mixup.
method Proposes a generalized mixup method with a flexible weighting scheme to preserve data structure.
result Preserves statistical properties of original data while maintaining model performance.
New method improves neural network generalization and robustness.
problem Improving neural network generalization and robustness to input perturbations.
method Co-Mixup with supermodular diversity, optimizing data saliency and diversity.
result Achieves state-of-the-art generalization, calibration, and localization results.
Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. By studying the mutual information between the function learned by a VAE on the original data and on the augmented data we show that MixUp distorts learned functions in a way …
ε-Consistent Mixup improves semi-supervised classification accuracy.
problem Improving semi-supervised classification accuracy with limited labeled data.
method Combines Mixup's linear interpolation with consistency regularization, using an adaptive tradeoff between the two.
result ε-Consistent Mixup yields the largest gains in low label-availability scenarios.
C-Mixup improves generalization in regression tasks by adjusting label similarity.
problem Improving generalization in regression tasks with limited data.
method C-Mixup adjusts the probability of mixing examples based on label similarity.
result C-Mixup achieves better generalization and robustness compared to vanilla mixup.
Unified framework models graph data as a mixture of graphons using graph moments.
problem Graph datasets often mix from multiple underlying distributions.
method Model graph data as a mixture of graphons, using graph moments to cluster graphs.
result Graphs from similar graphons exhibit similar motif densities, enabling principled estimation of graphon mixture components.
Puzzle Mix optimizes mixup by leveraging saliency and local statistics.
problem Overfitting and adversarial vulnerability of deep networks.
method Puzzle Mix alternates between multi-label and saliency-based objectives for optimal mixing masks.
result Puzzle Mix outperforms other mixup methods in generalization and adversarial robustness.
New loss improves DNN calibration without sacrificing accuracy.
problem Overfitting and overconfident predictions in DNNs.
method Proposes a new loss function inspired by Bayes decision theory.
result Improves DNN calibration without compromising accuracy.
Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has been shown to be a surprisingly effective method of data augmenta…
MixUp is a recently proposed data-augmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their labels. Training deep neural networks with such additional data is shown capable of significantly improving the predictive accuracy of the curren…
Mixup helps learn both features in multi-view classification problems.
problem Learning both features in multi-view classification problems.
method Training with a specific instantiation of Mixup.
result Training with Mixup leads to learning both features for every class.
The paper introduces boundary thickness as a measure for improving model robustness.
problem Improving the robustness of machine learning models to adversarial and non-adversarial corruptions.
method Introducing boundary thickness as a measure and showing how various procedures can increase it.
result Thicker decision boundaries lead to improved robustness against adversarial and out-of-distribution transforms.
DACL tackles domain-specific contrastive learning by using Mixup noise.
problem Domain-specific contrastive learning methods rely on data augmentation techniques that require domain knowledge.
method DACL uses Mixup noise to create similar and dissimilar examples without domain-specific data augmentation.
result DACL outperforms other domain-agnostic noising methods and combines well with domain-specific methods.
Data augmentation methods improve worst-case model performance.
problem Ensuring fair predictions across subpopulations in large models.
method Linear last layer retraining with data augmentation techniques.
result Optimal worst-group accuracy achieved for Gaussian latent representation distribution.
Machine learning assesses group collaboration in classrooms.
problem Assess student group collaboration in large classrooms.
method Deep learning models using Mixup data augmentation and ordinal-cross-entropy loss function.
result Improved assessment of group collaboration quality.
XMixup improves transfer learning accuracy by 1.9% with less training time.
problem Efficiently transfer knowledge from large source datasets to target tasks with small samples.
method Cross-domain Mixup technique that selects auxiliary samples from source datasets and augments training samples via mixup strategy.
result Improves accuracy by 1.9% on average over six real-world transfer learning datasets.
Temperature scaling fails for distributions with class overlaps, while Mixup improves calibration.
problem Temperature scaling's performance degrades with class overlaps, leading to poor calibration.
method Identified temperature scaling's limitations and compared it with Mixup for calibration.
result Mixup significantly outperforms temperature scaling in calibration metrics with class overlaps.
In this work, we explain the working mechanism of MixUp in terms of adversarial training. We introduce a new class of adversarial training schemes, which we refer to as directional adversarial training, or DAT. In a nutshell, a DAT scheme perturbs a training example in the direction of another example but keeps its ori…
A new probabilistic mixup framework improves deep learning generalization.
problem Improving deep learning generalization performance.
method Introduces a novel probabilistic mixup framework for conditional density estimation.
result Empirical results show benefits over existing mixup variants.
FED distills ensemble predictions efficiently, improving accuracy and uncertainty.
problem Efficiently distilling ensemble predictions from limited data.
method Proposes Functional Ensemble Distillation (FED) using mixup augmentation.
result Significantly boosts performance in accuracy and uncertainty estimation.
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their …
It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally non-linear behavior nearby input examples. Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial per…
Unified theory explains how data augmentation improves deep learning models.
problem Understanding why data augmentation improves model generalization.
method Unified theoretical framework explaining two key effects: partial semantic feature removal and feature mixing.
result Data augmentation enhances generalization through partial semantic feature removal and feature mixing.
We apply mixup regularization to RNNs for improved performance.
problem Improving the performance of Recurrent Neural Networks.
method We extend mixup regularization techniques to RNNs, making them easier and less computationally intensive.
result Sequence mixup improved F-1 score and reduced loss in Named Entity Recognition tasks.
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.
A new data augmentation method selects mixed classes based on class distances for better performance.
problem Improving recognition accuracy in object recognition using deep learning.
method Calculates class distances and selects mixed data from suitable classes dynamically.
result Improves recognition performance on general and long-tailed image recognition datasets.
New approach improves model robustness and calibration in latent space.
problem Improving model robustness and calibration under input perturbations.
method VarMixup (Variational Mixup) in latent space of VAEs.
result Models trained with VarMixup in latent space are more robust and calibrated.
Improves neural networks by adjusting labels to reduce overfitting to noisy data.
problem Reduces overfitting to mislabeled or non-representative samples in neural networks.
method Combines self-adaptive training with mixup to improve accuracy and robustness.
result Achieves state-of-the-art accuracy on image recognition datasets with label noise.
MixBoost generates synthetic instances to balance imbalanced datasets.
problem Training models on imbalanced datasets.
method Iterative data augmentation method that selects and combines instances from majority and minority classes.
result MixBoost outperforms existing approaches on 20 benchmark datasets.
RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.
problem Mixup degrades SSL performance by compromising artificial labels purity.
method RegMixMatch integrates high- and low-confidence samples, uses class-aware Mixup, and mitigates confirmation bias.
result RegMixMatch achieves state-of-the-art performance in SSL benchmarks.
New framework explains diverse impacts of data augmentation.
problem Understanding the varied effects of data augmentation on model performance.
method Developed a theoretical framework to characterize DA's impact on linear models.
result Data augmentation induces implicit spectral regularization through two effects.
In this work, we propose data augmentation methods for embeddings from pre-trained deep learning models that take a weighted combination of a pair of input embeddings, as inspired by Mixup, and combine such augmentation with extra label softening. These methods are shown to significantly increase classification accurac…
MixKD improves large-scale language model compression and generalization.
problem Inefficient and resource-intensive large-scale language models.
method MixKD uses mixup data augmentation to enhance student model's generalization ability.
result MixKD leads to significant performance gains over standard KD and competitive baselines.