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CelebA-HQ, ImageNet
CelebA-HQ, ImageNet -
ImageNet 64x64, ImageNet 128x128, LSUN 256x256
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used pre-trained DDPMs on ImageNet 64x64, ImageNet 128x128, and LSUN 256x256. -
Self-supervised and semi-supervised learning for GANs
Self-supervised and semi-supervised learning for GANs -
Semi-supervised conditional GANs
Semi-supervised conditional GANs -
High-Fidelity Image Generation With Fewer Labels
High-fidelity image generation with fewer labels -
MobileNetV1, MobileNetV2 and MNasNet models for ImageNet classification
The dataset used in this paper is the MobileNetV1, MobileNetV2 and MNasNet models for ImageNet classification. -
ImageNet, ADE20K, and COCO datasets
The dataset used for ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation. -
Learning generative visual models from few training examples
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories -
Autoencoders with Intrinsic Dimension Constraints
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations -
Synthetic Data from Diffusion Models Improves ImageNet Classification
Large-scale text-to-image diffusion models can be fine-tuned to produce class-conditional models with SOTA FID and Inception Score on ImageNet. -
ImageNet-1k and ImageNet-100
The dataset used in the paper is ImageNet-1k and ImageNet-100, which are large-scale image classification datasets. -
ImageNet trained PyTorch models under various simple image transformations
ImageNet trained PyTorch models are evaluated under various simple image transformations. -
DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks
The proposed DualConv is used to replace the standard convolution in VGG-16 and ResNet-50 to perform image classification experiments on CIFAR-10, CIFAR-100, and ImageNet datasets. -
ImageNet Object Detection Dataset
The ImageNet object detection dataset, which contains a sufficiently large number of images and object categories to reach a conclusion. -
Tiny-ImageNet-200
The dataset used in the paper is Tiny-ImageNet-200, which consists of 100k training, 10k validation, and 10k test images of dimensions 64x64x3. -
ImageNet VID dataset
The ImageNet VID dataset is a large scale benchmark for video object detection task consisting of 3,862 videos in the training set and 555 videos in the validation set. 30... -
ImageNet and CIFAR-10 datasets
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used VGG-16, ResNet-50, and MobileNet-v2 models on the ImageNet and CIFAR-10... -
ImageNet classification
ImageNet classification dataset, COCO dataset -
ImageNet2012
The dataset used in the paper for attention-oriented data analysis and attention-based adversarial defense. -
Geometry aware 3D generation from in-the-wild images in ImageNet
Generating accurate 3D models is a challenging problem that traditionally requires explicit learning from 3D datasets using supervised learning.
