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Deep-CAPTCHA
A deep learning based CAPTCHA solver for vulnerability assessment -
ML4H Findings Track Collection: Machine Learning for Health (ML4H) 2023
A synthetic dataset for training a family of Reinforcement Learning (RL) methods to build explainable pathways for the differential diagnosis of anemia, as a primary use case. -
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer...
Modern biomedical image analysis using deep learning often encounters the challenge of limited annotated data. To overcome this issue, deep generative models can be employed to... -
Nested Hierarchical Transformer
The dataset used in the paper is not explicitly mentioned, but it is implied to be ImageNet and CIFAR-10/100. -
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... -
Low-dose CT image denoising dataset
The dataset used for training and testing deep neural networks-based denoising models for CT imaging. -
BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion
The paper proposes a Bagging Deep Learning Training Framework (BEND) based on efficient neural network diffusion. -
Spiking Neural Network Dataset
The dataset used in this paper is a spiking neural network (SNN) with 20 layers, where each layer has 2000 LIF neurons. The input spikes are Poisson trains at a target rate of... -
Towards stable and efficient training of verifiably robust neural networks
A method for learning models robust to adversarial examples. -
Deep learning for 3D building reconstruction: A review
Deep learning for 3D building reconstruction: A review -
Deep Learning Models
The dataset used in this paper is a set of 20 well-known deep-learning models, including AlexNet, ResNet, VGG, DenseNet, etc. -
DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout
The paper proposes a technique for domain adaptation in stacked autoencoders using systematic dropout. -
Learning the number of neurons in deep networks
Learning the number of neurons in deep networks. -
Hardware-Aware Latency Pruning
The proposed hardware-aware latency pruning (HALP) paradigm. Considering both performance and latency contributions, HALP formulates global structural pruning as a global... -
PERMUTOHEDRAL LATTICE CONVOLUTION
The permutohedral lattice convolution is used to process sparse input features, allowing for efficient filtering of signals that do not lie on a dense grid. -
ResNet18 dataset
The dataset used in the paper is the ResNet18 dataset, which is a convolutional neural network dataset. -
Joint Visual Denoising and Classification Using Deep Learning
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two... -
Atari 2600 games
The dataset used in this paper is a collection of state-action pairs generated by a pre-trained RL agent, used to train a self-supervised interpretable network (SSINet) to... -
Progressive Feedforward Collapse of ResNet Training
The dataset used in the paper is a ResNet trained on various datasets, including MNIST, Fashion MNIST, CIFAR10, STL10, and CIFAR100. -
COVID-CT-Dataset: a CT scan dataset about COVID-19
A CT scan dataset about COVID-19
