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Showing 1 to 20 of 36 for “"CIFAR"”.
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Entrópia alapú bizonytalanságbecslés hatása öncímkézésen alapuló félig felügyelt képosztályozásban
… küszöbön alapuló szelekció. A vizsgálatok a CIFAR-10, a CIFAR-100 és az SVHN képosztályozási adathalmazokon történnek.
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Resource and data optimization for hardware implementation of deep neural networks targeting FPGA-based edge devices
… and 653.5 μs for classifying one image in the Cifar-10 data set using the CifarNet. For the LeNet we were able to maintain high accuracy of 97.6% for the MNIST data set and 83.4% for the Cifar-10 data set. We achieved the best single-image latency, 5.2x faster for the LeNet and 1.95x faster for …
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Exploring Accumulated Gradient-Based Quantization and Compression for Deep Neural Networks
… dataset), LeNet-5 (MNIST dataset), AlexNet (CIFAR-10 dataset) and VGG-16 (CIFAR-10 dataset), post-training quantization achieves 7.62x, 10.87x, 6.39x and 12.43x compression, in-training quantization achieves 22.08x, 21.05x, 7.95x and 12.71x compression and combined quantization achieves …
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Neural Network Pruning for ECG Arrhythmia Classification
… methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning algorithm on ECG, finetuning time can be …
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The Low-rank Simplicity Bias in Deep Networks
… bias, improving generalization performance on CIFAR and ImageNet without changing the modeling capacity.
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Are Particle-Based Methods the Future of Sampling in Joint Energy Models? A Deep Dive into SVGD and SGLD
… in energy-based models. Experiments on the CIFAR-10 dataset demonstrate that SGLD, particularly with Sharpness-Aware Minimization (SAM), outperforms SVGD in classification accuracy. However, SVGD without SAM, despite its lower classification accuracy, exhibits lower calibration error …
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Brain Tumor Classification Using Hit-or-Miss Capsule Layers
… and accurate diagnosis. While datasets like CIFAR, ImageNet, and SVHN have tens of thousands, hundreds of thousands, or millions of samples, an MRI dataset may not have the same luxury of receiving accurate labels for each image containing a tumor. This work covers three models that classify …
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Deep adaptive anomaly detection using an active learning framework
… the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be …
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Data Centric Defenses for Privacy Attacks
… offer a better privacy-utility tradeoff on CIFAR-10 for membership inference, they reduce the reconstruction rate to ≤ 1% while reducing the classification accuracy by only 2% against model inversion attacks. This is the first attempt to defend model inversion and membership inference …
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Hardware software co-design of machine learning accelerators using univariate functions
… Applying this to an MLP-based vision model on CIFAR-10, we cut the number of operations by 45%–28%, boosting hardware efficiency. We validate PSA with two hardware accelerators while maintaining the same reference accuracy. First, an unrolled streaming design that reduces LUT + DSP usage by 25% …
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P2P Based Personalized Federated Learning for Collaborative Model Sharing and Inferencing
… the whole FL model. Experiments on the MNIST and CIFAR-10 datasets demonstrate that this P2P strategy generates more precise models than random client communication.
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Mathematical Connections between Convolutional Neural Networks and the Scattering Transformation
… the scattering transformation on both the CIFAR-10 dataset and a publicly available dataset from TensorFlow (datasets.bee_dataset). Results include similar (though at times decreased) accuracy but improved stability compared to that of two Keras Conv2D and MaxPooling2D layers.
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Deep learning of visual features with limited supervision.
… we achieve 77% accuracy on imbalanced CIFAR-10 using only 500k pre-training samples—outperforming full ImageNet pre-training. On Penn Action video classification, our contrastive learning model reaches 76% accuracy, significantly surpassing ViViT model. In active learning on aquatic …
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Distributed Machine Learning in Heterogeneous Edge Networks
… as ResNet-56 and ResNet-110, using datasets like CIFAR-10, CIFAR-100, CINIC-10, and HAM10000, validate its efficacy. Results demonstrate up to an 80\% reduction in training time compared to advanced federated learning methods while maintaining model accuracy. DTFL reduces training time in both IID …
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Image Compression using Sum-Product Networks
… we deal with naturally occurring data, e.g., CIFAR-10 images, the PGM underlying the source data is unknown. Existing structure learning algorithms for PGMs are inefficient for learning from large datasets and place additional constraints on the graphical model structure that diminishes a …
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TwinDNN: A tale of two deep neural networks
… our design on two image classification tasks: CIFAR-10 and ImageNet. Our experiments show that our design can recover up to 94% of accuracy drop caused by extreme network compression, with more than 90% increase in throughput compared to just using the original DNN. This is more than 17% extra …
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Multi-model-based defense against adversarial examples for neural networks
… in a white-box attack scenario among MNIST, CIFAR-10, and Tiny ImageNet datasets, while maintaining similar accuracy on legitimate examples. Our general framework can also inspire rich future research to construct a desirable model family achieving higher robustness diversity.
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Query K-means clustering for crowdsourcing
… synthetic and real datasets, including MNIST and CIFAR $10$.
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Game of threads: Enabling asynchronous poisoning attacks
… degradation and model biasing attacks on the CIFAR-10 image recognition task using ResNet-style DNNs, attacking an asynchronous training implementation published by PyTorch. We perform a deeper analysis on a LeNet-style DNN. We also perform proof-of-concept experiments to validate our …
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All Analog CNN Accelerator with RRAMs for Fast Inference
… of transistors. This circuit classifies one Cifar-10 dataset image in 6µs (160k frames/s) with 2.4µJ energy per classification with an accuracy of 85%. It contains 7.5 million MAC units and achieves 5 million MAC/mm².
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