Abstract: Most dataset distillation methods struggle to accommodate large-scale datasets due to their substantial computational and memory requirements. Recent research has begun to explore scalable ...
I’ve been working with the ANN2SNN conversion in mlGeNN, but I’m having trouble executing the models that use the ImageNet dataset, specifically, resnet32_imagenet_train_tf.py, resnet32_imagenet.py, ...
Visual generation frameworks follow a two-stage approach: first compressing visual signals into latent representations and then modeling the low-dimensional distributions. However, conventional ...
ABSTRACT: Diabetic retinopathy is a serious concern for people dealing with diabetes. Detecting diabetic retinopathy poses significant challenges, requiring skilled professionals, extensive manual ...
Getting zero accuracy on the ImageNet dataset. #125 New issue Open shahidalihakro ...
Introduction: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. Despite significant research, AD ...
Detailed information about TV100, including the data collection process, the country distribution, and class distribution. It also contains an empirical evaluation of zero-shot and finetuned ...
Training a Large CNN for Image Classification: Researchers developed a large CNN to classify 1.2 million high-resolution images from the ImageNet LSVRC-2010 contest, spanning 1,000 categories. The ...
Abstract: Texture perception plays a vital role in various fields, from computer vision to geology, influencing object recognition, image segmentation, and rock classification. Despite advances in ...
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