Archives for CNNs - Page 2
T2T-ViT employs progressive tokenization that takes patches of an image and converts it into an overlapped-token over a few iterations
The post Complete Guide to T2T-ViT: Training Vision Transformers Efficiently with Minimal Data appeared first on Analytics India Magazine.
T2T-ViT employs progressive tokenization that takes patches of an image and converts it into an overlapped-token over a few iterations
The post Complete Guide to T2T-ViT: Training Vision Transformers Efficiently with Minimal Data appeared first on Analytics India Magazine.
RepVGG is a simple ConvNet architecture that combines multibranch topologies’ increased performance and the simplicity of VGG topology.
The post RepVGG: Can You Make Simple Architectures Great Again? appeared first on Analytics India Magazine.
TransUNet, a Transformers-based U-Net framework, achieves state-of-the-art performance in medical image segmentation applications
The post Hands-on TransUNet: Transformers For Medical Image Segmentation appeared first on Analytics India Magazine.
While convolutional neural networks (CNNs) have dominated the field of object recognition, it can easily be deceived by creating a small perturbation, also known as adversarial attacks. This can lead to the failure of the computer vision models and make it susceptible to cyberattacks. CNN’s vulnerability to image perturbations has become a pressing concern for…
The post How Is Neuroscience Helping CNNs Perform Better? appeared first on Analytics India Magazine.
While convolutional neural networks (CNNs) have dominated the field of object recognition, it can easily be deceived by creating a small perturbation, also known as adversarial attacks. This can lead to the failure of the computer vision models and make it susceptible to cyberattacks. CNN’s vulnerability to image perturbations has become a pressing concern for…
The post How Is Neuroscience Helping CNNs Perform Better? appeared first on Analytics India Magazine.
While convolutional neural networks (CNNs) have dominated the field of object recognition, it can easily be deceived by creating a small perturbation, also known as adversarial attacks. This can lead to the failure of the computer vision models and make it susceptible to cyberattacks. CNN’s vulnerability to image perturbations has become a pressing concern for…
The post How Is Neuroscience Helping CNNs Perform Better? appeared first on Analytics India Magazine.
VIBE - Video Inference for 3D Human Body Pose and Shape Estimation. It uses CNNs, RNNs(GRU) and GANs along with a self-attention layer to achieve its state-of-the-art results.
The post Guide To VIBE: Video Inference for 3D Human Body Pose and Shape Estimation appeared first on Analytics India Magazine.
VIBE - Video Inference for 3D Human Body Pose and Shape Estimation. It uses CNNs, RNNs(GRU) and GANs along with a self-attention layer to achieve its state-of-the-art results.
The post Guide To VIBE: Video Inference for 3D Human Body Pose and Shape Estimation appeared first on Analytics India Magazine.
Convolutional Neural Networks (CNNs) are one of the most important neural network algorithms in the present scenario. Tech giants like Google, Facebook, Amazon have been thoroughly using this neural network to perform and achieve a number of image-related tasks. The applications of CNNs mostly includes the field of computer vision for image recognition, object detection,…
The post 10 Free Online Resources To Learn Convolutional Neural Networks appeared first on Analytics India Magazine.