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6 results for “CIFAR-10”
Semantic-Discrepant Outliers on CIFAR-10 Dataset
<p>We provide synthetic Out-of-distibution (OOD) dataset, which is called Semantic-Discrepant (SD) outliers, on CIFAR-10 dataset. SD outliers can be utilized for boosting OOD detection model performance. For the details, SD outliers are realistic OOD samples that contains incoherent semantic shift while preserving nuisances with in-distribution (ID). SD-outliers are generated from ID training samples using semantic-discrepant sampling in the diffusion model. so SD-outliers on CIFAR-10 contains 50000 32X32 images which is same as CIFAR-10 training dataset size. The dataset has a capacity of 768MB.</p>
Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-10
<p><strong>Abstract</strong></p> <p>In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p>The complete zoo is 2.3TB large. Due to the size, this repository contains the checkpoints of epochs 1, 10 and 50. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.</p> <p> </p>
Performance Measurement Dataset Example for Extra-Deep CIFAR-10
<p>Sample dataset from the CIFAR-10 benchmark for Extra-Deep. The zipped folders contain the source file measurements in .qdrep format as recorded with NsightSystems.</p>
datos CIFAR-10 agregados para experimentación en Matlab con CNNs
<p>El fichero .mat contiene los datos agregados en matrices (data, labels) para training y (data_test, labels_test) para test. Se proporciona un pequeño script para entrenamiento de CNNs con estos datos. La referencia a la pagina web original está incluida: https://www.cs.toronto.edu/~kriz/cifar.html</p>
CIFAR-10-R dataset
<p>Towards Realistic Out-of-Distribution Detection: A Novel Evaluation Framework for Improving Generalization in OOD Detection:</p> <p>This paper presents a novel evaluation framework for Out-of-Distribution (OOD) detection that aims to assess the performance of machine learning models in more realistic settings. We observed that the real-world requirements for testing OOD detection methods are not satisfied by the current testing protocols. They usually encourage methods to have a strong bias towards a low level of diversity in normal data. To address this limitation, we propose new OOD test datasets (CIFAR-10-R, CIFAR-100-R, and ImageNet-30-R) that can allow researchers to benchmark OOD detection performance under realistic distribution shifts. Additionally, we introduce a Generalizability Score (GS) to measure the generalization ability of a model during OOD detection. Our experiments demonstrate that improving the performance on existing benchmark datasets does not necessarily improve the usability of OOD detection models in real-world scenarios. While leveraging deep pre-trained features has been identified as a promising avenue for OOD detection research, our experiments show that state-of-the-art pre-trained models tested on our proposed datasets suffer a significant drop in performance. To address this issue, we propose a post-processing stage for adapting pre-trained features under these distribution shifts before calculating the OOD scores, which significantly enhances the performance of state-of-the-art pre-trained models on our benchmarks.</p>
CIFAR-10 & CIFAR-100
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