Pytorch Cnn Visualizations

Pytorch Cnn Visualizations - visualize PyTorc… | Awesome Repos

Features

Neural Network Interpretability - Provides a comprehensive toolkit for analyzing and visualizing CNN internal workings to understand model decision-making.
Class-Activation Optimization - Synthesizes representative images by iteratively optimizing input pixels to maximize target class activations.
CNN Kernel Visualizations - Visualizes convolutional filters and feature maps to reveal patterns detected by specific network layers.
Iterative Image Optimizers - Uses iterative gradient descent on image pixels to amplify specific convolutional filter responses.
Neural Network Feature Auditing - Decomposes internal activations to identify the visual patterns and conceptual features detected by convolutional filters.
Class Activation Map Generation - Provides the generation of spatial heatmaps to highlight image regions driving specific class predictions.
Saliency Mapping - Generates heatmaps that highlight the specific image regions most influential to a model's classification.
Pixel Saliency Maps - Implements pixel-level saliency maps to identify influential image regions using backpropagation.
Filter Response Visualizations - Optimizes input images to reveal the patterns and features detected by specific convolutional layers and filters.
Gradient Flow Analysis - Analyzes the flow of gradients across network layers to improve model transparency and performance.
Feature Amplification Implementations - Implements a tool for amplifying image patterns by optimizing input pixels to maximize filter activations.
Activation Inversion - Implements layer-activation inversion to recover input representations from internal network activations.
Layer Interpretation Tools - Provides tools to reconstruct input representations by interpreting the activation patterns of internal layers.
Image Generation and Synthesis - Creates synthetic representative images and amplifies perceived patterns using Deep Dream techniques.
Class-Activation Synthesis - Generates synthetic images that maximize the activation of target classes through input optimization.
Explainability Map Smoothing - Reduces noise in interpretability heatmaps by averaging gradients over images augmented with Gaussian noise.
Computer Vision - Techniques for visualizing convolutional neural network features.
Explainable AI Libraries - Visualization techniques for PyTorch convolutional neural networks.
Convolutional Neural Networks (CNNs) - Listed in the “Convolutional Neural Networks (CNNs)” section of the The Incredible Pytorch awesome list.
To be Classified - Listed in the “To be Classified” section of the The Incredible Pytorch awesome list.
Visualization - Listed in the “Visualization” section of the The Incredible Pytorch awesome list.

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