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Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches t...
A deep learning model that learns subject-level representation from a set of local features. The model represents the image volume as a bag (or set) of local features and can... -
Waste Classification using Computer Vision and Deep Learning
Dataset for waste classification using computer vision and deep learning -
MobileNetV2
The dataset used in this paper is a MobileNetV2 model, which is a type of deep neural network. The dataset is used to evaluate the performance of the proposed heterogeneous system. -
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and...
The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used a range of datasets, including CIFAR-10, GTSRB, Google Speech Command,... -
Various Datasets
The datasets used in the paper are described as follows: WikiMIA, BookMIA, Temporal Wiki, Temporal arXiv, ArXiv-1 month, Multi-Webdata, LAION-MI, Gutenberg. -
INFOBATCH: LOSSLESS TRAINING SPEED UP BY UNBIASED DYNAMIC DATA PRUNING
Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training. -
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
The dataset used in this paper is a CNN training dataset, specifically VGG-16, VGG-19, and AlexNet. -
Learning Multiple Layers of Features from Tiny Images
The CIFAR-10 dataset consists of 60,000 training images and 10,000 test images. Each image is a 32×32 color image.
