In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10. It was compiled by combining CIFAR-10 with images selected and downsampled from the ImageNet database. We present the approach to compiling the dataset, illustrate the example images for different classes, g…
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48 results for “CIFAR 10”
Improved image classification accuracy on CIFAR-10 dataset.
problem Classifying images from the CIFAR-10 dataset with high accuracy.
method Combining features from manual and deep learning approaches, including VGG16, Inception ResNet v2, HOG, and pixel intensities.
result Achieved 94.6% testing accuracy by combining top 1000 principal components.
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared …
This paper explores normalization in neural ODEs, achieving high accuracy in CIFAR-10.
problem Understanding the role of normalization in neural ODEs.
method Investigated different normalization techniques and their impact on neural ODEs performance.
result Achieved 93% accuracy in CIFAR-10 classification task.
Convolutional DKMs improve kernel methods on MNIST, CIFAR-10, and CIFAR-100.
problem Improving kernel methods for image classification.
method Developed a novel inter-domain inducing point approximation and introduced various techniques to extend DKMs to convolutional networks.
result Achieved state-of-the-art performance on image classification benchmarks.
Goldilocks activation functions improve neural network performance.
problem Improving neural network performance and understanding signal transformation.
method Introducing Goldilocks activation functions that locally deform input signals.
result Goldilocks networks outperform or match SELU and RELU on CIFAR-10 and CIFAR-100 datasets.
Empirical study on adversarial robustness in transfer learning from CIFAR 100 to CIFAR 10.
problem Evaluating adversarial robustness in transfer learning from CIFAR 100 to CIFAR 10.
method Study of adversarial robustness under transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10, using robust optimisation.
result Using PGD examples during training on the source task leads to more general robust features that are easier to transfer and achieve 5.2% more accuracy against white-box PGD attacks.
Res-SE-Net boosts Resnet performance by enhancing bridge-connections.
problem Reduced accuracy in Resnet due to lack of feature map contribution from bridge-connections.
method Proposed Res-SE-Net architecture with Squeeze-and-Excitation (SE) block to quantify and weight feature map importance.
result Res-SE-Net generalizes better than Resnet and SE-Resnet on CIFAR-10 and CIFAR-100 datasets.
Generative models improve adversarial robustness by adding synthetic data.
problem Improving robustness in machine learning models trained on limited data.
method Using synthetic data generated from a large dataset to augment the original training set.
result Generative models can significantly reduce the robust-accuracy gap compared to models trained with additional real data.
This paper explores adversarial training limits and improves model robustness against norm-bounded perturbations.
problem Understanding and improving adversarial robustness of deep neural networks.
method Systematic study of adversarial training with various factors, including model size, activation functions, and unlabeled data.
result Training robust models that go beyond state-of-the-art results by combining larger models, Swish/SiLU activations, and model weight averaging.
CEB enhances model resilience through simple entropy bottleneck.
problem Improving model robustness against adversarial attacks.
method Conditional Entropy Bottleneck (CEB) combined with data augmentation.
result CEB significantly boosts adversarial robustness on various benchmarks.
Paper introduces MultiTargeted testing, improving adversarial testing efficiency.
problem Improving efficiency and effectiveness of adversarial testing methods.
method Introduces MultiTargeted testing, using alternative surrogate losses.
result MultiTargeted outperforms other PGD-based methods, requiring fewer iterations.
A new pruning method reduces neural network computation without retraining.
problem Efficiently reduce neural network computation while maintaining accuracy.
method Structured directional pruning via perturbation orthogonal projection.
result Achieves state-of-the-art pruned accuracy without retraining.
Machine learning is currently dominated by largely experimental work focused on improvements in a few key tasks. However, the impressive accuracy numbers of the best performing models are questionable because the same test sets have been used to select these models for multiple years now. To understand the danger of ov…
A new semi-supervised learning method using label gradients.
problem Improve accuracy in semi-supervised learning with limited labeled data.
method Impute labels for unlabeled data using a distance metric based on model gradients, then optimize these imputed labels.
result Demonstrates state-of-the-art accuracy in semi-supervised CIFAR-10 classification.
Super-AND improves unsupervised embedding learning with 89.2% accuracy on CIFAR-10.
problem Extracting good representations from data without labels.
method Super-AND uses unique losses to gather similar samples and maintain features.
result Super-AND achieves 89.2% accuracy on CIFAR-10 image classification.
PBA generates nonstationary augmentation schedules to match AutoAugment's performance with less compute.
problem Choosing an effective augmentation policy from a large search space.
method Population Based Augmentation (PBA) generates nonstationary augmentation policy schedules.
result PBA matches AutoAugment's performance on CIFAR-10, CIFAR-100, and SVHN with less compute.
Paper shows adversarial training can be fooled by new type of noise.
problem Adversarial training can be fooled by new types of noise.
method Designing ADVIN, a new type of inducing noise.
result ADVIN can degrade adversarial training robustness by 99.9%.
Framework combines adversarial training and provable robustness for neural networks.
problem Training certifiably robust neural networks with provable robustness guarantees.
method Formulates joint optimization problem with adversarial and provable robustness objectives; develops gradient-descent technique.
result Consistently matches or outperforms prior approaches for provable l infinity robustness on MNIST and CIFAR-10.
Unified view of contrastive and supervised learning improves model performance.
problem Improving model performance in classification, robustness, and detection.
method Hybrid discriminative-generative training of energy-based models.
result Improved performance on classification, robustness, out-of-distribution detection, and calibration.
Colored noise improves neural network robustness against adversarial attacks.
problem Vulnerability of neural networks to adversarial perturbations.
method Injection of colored noise into network weights and activations during adversarial training.
result Our approach outperforms previous methods in terms of adversarial accuracy on CIFAR-10 and CIFAR-100 datasets.
New test sets show current image classifiers struggle with harder images.
problem Current image classifiers overfit to original test sets.
method Created new test sets for CIFAR-10 and ImageNet, evaluated various models.
result Accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet.
Improved sample efficiency with normalized RBF kernels in neural networks.
problem Learning more with less data in deep learning models.
method Two-phase method to train neural networks with normalized RBF kernels as output layer.
result Normalized RBF kernel networks achieve higher sample efficiency, compactness, and separability.
EENA efficiently searches neural architectures with minimal resources.
problem Lack of direction and high computational cost in neural architecture search.
method EENA uses guided evolution with mutation and crossover operations.
result EENA designs highly effective neural architectures with minimal resources.
SPAT improves adversarial robustness by preserving semantics in adversarial training.
problem Adversarial examples often have different semantics than original data, introducing unintended biases.
method Semantics-preserving adversarial training (SPAT) that encourages pixel perturbation shared among all classes.
result SPAT improves adversarial robustness and achieves state-of-the-art results in CIFAR-10 and CIFAR-100.
New method enhances adversarial robustness of deep learning models.
problem Improving the robustness of deep learning models against adversarial attacks.
method Optimal transport regularized divergences applied to distributionally robust optimization.
result Improved adversarial robustness on CIFAR-10 and CIFAR-100 datasets.
New bound on neural network generalization error using geometric complexity.
problem Understanding the generalization capabilities of deep neural networks.
method Derive a new upper bound on generalization error using margin-normalized geometric complexity.
result Empirical validation of the bound for ResNet-18 on CIFAR-10 and CIFAR-100 datasets.
Improved deep kernel machine achieves 94.5% test accuracy on CIFAR-10.
problem Achieving high accuracy on complex tasks like image classification.
method Stochastic kernel regularisation to add noise to learned Gram matrices.
result 94.5% test accuracy on CIFAR-10.
KD technique improves QDNN performance with reduced hyper-parameters.
problem Restoring performance loss in QDNNs due to quantization.
method Applied KD with reduced hyper-parameters, including a new coefficient reduction technique.
result Achieved 92.7% test accuracy on CIFAR-10 and 67.0% on CIFAR-100 with 2-bit weights.
Score-based generative models achieve state-of-the-art classification accuracy on CIFAR-10.
problem Improving classification accuracy on natural image datasets.
method Score-based generative models applied to CIFAR-10.
result Score-based models achieve state-of-the-art classification accuracy on CIFAR-10.
Method detects and relabels noisy image labels to improve DNN performance.
problem Label noise in training images harms DNN generalization.
method Identifies noisy labels based on predictive uncertainty changes over training.
result Iterative relabeling of noisy labels improves DNN performance.
Proposes a framework for semi-supervised continual learning from sequentially arriving data.
problem Learning from data with changing task distribution over time, especially in domains with a mix of labeled and unlabeled data.
method Meta-Consolidation for Continual Semi-Supervised Learning (MCSSL) framework with a hypernetwork and semi-supervised auxiliary classifier.
result Significant improvements in continual semi-supervised learning setting.
In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet. The first branch receives supervision signal and is dedicated to the extraction of invariant class-related representations. The s…
Patch augmentation boosts neural network accuracy and robustness.
problem Improving neural network accuracy and robustness to adversarial attacks.
method Data augmentation technique creating new images by superimposing patches from image pairs.
result Significant accuracy improvements on CIFAR-10 and CIFAR-100 datasets.
PixelCNN models can achieve state-of-the-art results on CIFAR-10 with exact likelihood computation.
problem Dequantization gap in modeling discrete data like images.
method Introducing subset flows to allow exact computation of likelihoods for discrete data.
result PixelCNN models trained with exact likelihood computation achieve state-of-the-art results on CIFAR-10.
The paper investigates how class mean vectors enhance neural network classification.
problem Improving neural network performance in classification tasks.
method Exploring the role of class mean vectors in neural networks, including direct computation of weights, performance monitoring, and self-training.
result Empirical evidence suggests that using class mean vectors can significantly improve neural network performance on classification tasks.
Unlabeled data improves adversarial robustness in machine learning.
problem Improving robustness of machine learning models against adversarial attacks.
method Semisupervised learning with self-training using unlabeled data.
result Semisupervised learning with unlabeled data significantly improves adversarial robustness.
A new mutual information optimization method using self-supervised binary contrastive learning.
problem Improving self-supervised contrastive learning for better model performance.
method Proposes a novel loss function for contrastive learning that optimizes mutual information in positive and negative pairs.
result The proposed method outperforms state-of-the-art self-supervised contrastive frameworks on various benchmark datasets.
Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator. We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearan…
EvalGAN evaluates GANs by measuring reconstruction quality and likelihood.
problem Evaluating the quality of generated images from GANs.
method EvalGAN uses a test set to measure reconstruction quality and likelihood in the original sample space.
result EvalGAN provides a direct and agnostic method for evaluating GANs.
Minimax defense improves neural network security against gradient-based attacks.
problem Gradient-based adversarial attacks on neural networks.
method Minimax optimization in a GAN framework to create a discriminator that plays a minimax game with the generator.
result Minimax defense significantly reduces adversarial attack success rates compared to standard classifiers.
New distance metric for neural architecture search reduces search space complexity.
problem Reducing the complexity of neural architecture search.
method Fisher task distance for measuring task similarity and online neural architecture search.
result Reduced search space complexity for task-specific architectures.
Proves bounds on deep CNN generalization error.
problem Understanding how well deep CNNs perform on unseen data.
method Bounds on generalization error based on training loss, parameters, Lipschitz constant, and weight distance.
result Bounds are independent of input size and feature map dimensions.
Study finds 10% redundant images in image classification datasets.
problem Redundancy in large image classification datasets.
method Analysis of CIFAR-10 and ImageNet datasets to identify redundant images.
result 10% of images are redundant and can be removed without significant loss of performance.
Dual Student separates the teacher from the student in SSL, improving performance.
problem Performance bottleneck caused by coupled teacher in consistency-based SSL methods.
method Introduces Dual Student, replacing the teacher with another student and defining a stabilization constraint.
result Significant improvement in classification performance on SSL benchmarks.
Study improves top-k set prediction with low cardinality.
problem Improving top-k set prediction accuracy with low cardinality.
method Introduces new target loss function and surrogate losses.
result Demonstrates effectiveness of cardinality-aware algorithms.
ThriftyNet uses a single convolutional layer recursively to maximize parameter usage.
problem Maximizing the use of parameters in deep convolutional neural networks.
method A single convolutional layer is used recursively, with normalization, non-linearities, downsampling, and shortcuts to maintain model expressivity.
result ThriftyNet achieves competitive performance with significantly fewer parameters.
Some theoretical improvements on the tightness of PAC-Bayes risk certificates for neural networkscs.LG
The paper improves risk certificate tightness for neural networks using PAC-Bayes bounds.
problem Improving the usability of risk certificates for neural networks based on PAC-Bayes bounds.
method Theoretical contributions including KL divergence bounds, efficient methodology for optimization, and methods for optimizing non-differentiable objectives.
result First non-vacuous generalization bounds on CIFAR-10 for neural networks.