Media Summary: In machine learning, it is helpful to identify the significant features of the input (e.g., pixels for images) that affect the model's ... In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate ... Presentation of the paper published in IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP20). Outline: ...
Smoothgrad Explained How Adding Noise - Detailed Analysis & Overview
In machine learning, it is helpful to identify the significant features of the input (e.g., pixels for images) that affect the model's ... In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate ... Presentation of the paper published in IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP20). Outline: ... This video is part of the Udacity course "Computational Photography". Watch the full course at ... Today, the way deep learning networks work is not easily understood. From the surface, we feed it with data, called network inputs ... Adversarial examples are inputs crafted to confuse neural networks, causing them to misclassify a given input. These examples ...
![[DEMO] Feature attribution visualization with SmoothGrad | FYP](https://i.ytimg.com/vi/usek6JsmxCE/mqdefault.jpg)
![[CVPR 2023] Initialization Noise in Image Gradients and Saliency Maps](https://i.ytimg.com/vi/4qshf2hZiis/mqdefault.jpg)
![[DEMO] Adversarial perturbation visualization with SmoothGrad | FYP](https://i.ytimg.com/vi/c2gi0rWGiFU/mqdefault.jpg)
