-
EncodeNet: A Framework for Boosting DNN Accuracy
Image classification is a fundamental task in computer vision, and the quest to enhance DNN accuracy without inflating model size or latency remains a pressing concern. -
CIFAR-10 Image Classification Using Feature Ensembles
CIFAR-10 image classification using feature ensembles -
ImageNet-1k dataset
The ImageNet-1k dataset is used as the backbone network for the proposed DEYOv2 model. -
CrowdHuman validation dataset
The CrowdHuman validation dataset contains 4370 images of people in highly crowded settings. -
MS COCO mini-val dataset
The MS COCO mini-val dataset contains 5000 images of 80 classes. -
Street View House Numbers
The street view house number recognition task involves transcribing an image with house numbers to a string of digits. -
CIFAR-100 and SVHN
The dataset used in the paper is CIFAR-100 and SVHN, which are image classification datasets. -
Binary Image Classification Dataset
The dataset used in the paper is a binary image classification dataset, containing images of size 2x2, 4x4, and 8x8. -
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Imag...
The proposed framework for image classification tasks, using a hypersphere representation of deep features. -
MNIST, Fashion-MNIST, CIFAR-10, and CelebA
The dataset used in the paper is not explicitly described, but it is mentioned that the authors pre-trained GANs on four datasets: MNIST, Fashion-MNIST, CIFAR-10, and CelebA. -
Clothing1M
Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade... -
ImageNet V2
The dataset used in the paper is ImageNet V2, a large-scale image classification dataset. -
ILSVRC 2012 Classification Challenge
The ILSVRC 2012 classification challenge dataset was used to evaluate the proposed method. -
ConvNeXt-T and ConvNeXt-S
The dataset used in this paper is the ConvNeXt-T and ConvNeXt-S datasets, which are variants of the ConvNeXt model. -
ResNet-20 and ResNet-32
The dataset used in this paper is the ResNet-20 and ResNet-32 datasets, which are variants of the ResNet-50 model. -
Imagenette and Imagewoof
The dataset used in this paper is the Imagenette and Imagewoof datasets. -
ResNet-20, ResNet-32, ResNet-56, and ResNet-110
The dataset used in the paper is ResNet-20, ResNet-32, ResNet-56, and ResNet-110 for image classification. -
CIFAR-10, CIFAR-100, and CelebA
The dataset used in the paper is CIFAR-10 and CIFAR-100 for image classification, and CelebA for image-to-image translation.
