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Evaluation Metrics Accuracy, Precision, and Recall Positive (Predicted Positive) Negative (Predicted Negative) True (Actual Positive) TP TN False (Actual Negative) FP FN For positive cases, the calculations are: Accuracy (ACC): (Acc= \frac{TP+TN}{TP+TN+FP+FN}) Precision: (\frac{TP}{TP+FP}) Recall: (\frac{TP}{TP+FN}) F1 Score: (\fr ...

Project OverviewGeneFace++ is a PyTorch-based deep learning framework that enables real-time audio-driven 3D facial animation synthesis. The system generates synchronized lip movements and facial expressions from audio input, creating realistic virtual character videos. The project repository is available at https://github.com/yerfor/GeneFacePl ...

LSTM Background For detailed coverage of LSTM core concepts, internal structure, and backpropagation derivation, there are many existing in-depth resources available. A high-level understanding of how LSTMs store and propagate information is sufficient to work through this implementasion. PyTorch Environment Setup When configuring PyTorch in a ...

Understanding PyTorch for Deep Learning PyTorch has emerged as one of the leading frameworks in deep learning, particularly favored in research and academia. Its dynamic computation graph and intuitive design make it a preferred choice for prototyping and experimentation. In contrast to static-graph alternatives, PyTorch enables developers to m ...

Environment and Hardware Configuration To ensure efficient computation, the environment is configured to utilize available GPU resources dynamically. Non-critical warnings are suppressed to maintain a clean log output. import os import pathlib import warnings import tensorflow as tf import matplotlib.pyplot as plt # Configure GPU memory growth ...

Model Parameter Profiling Utility functions in torch_utils facilitate the analysis of model complexity, including layer counts, parameter volumes, and computational load (FLOPs). The following snippet demonstrates how to aggregate parameter statistics and estimate floating-point operations using a dummy input tensor aligned with the model's str ...

Since AlexNet's introduction in 2012, convolutional neural networks have become widely adopted in computer vision tasks. As performance requirements increased, researchers developed deeper architectures like VGG, GoogLeNet, ResNet, and DenseNet. However, these deeper networks introduced significant efficiency challenges: Storage Requirements: ...

Table of Contents Article Overview Framework Workflow Code Analysis Article Overview Problem Context: Hyperspectral compressive imaging utilizes compressed sensing theory to capture hyperspectral data through snapshot measurements via coded apertures, avoiding temporal scanning. The core challenge lies in reconstructing the original hypers ...

Training Deep Networks Why do we need batch normalization layers? Let us review some practical challenges that arise when training neural networks. First, the way data are preprocessed often dramatically influences the final result. Recall the example of using a multilayer perceptron to predict house prices. When working with real data, our fir ...

01 Cat vs Dog Recognition Introduction: Manually building LeNet for cat vs dog recognition. Reference: https://mtyjkh.blog.csdn.net/article/details/121263237 Code: 01-cat-dog (github.com) Note: Beginners are advised to practice typing all the code, as it serves as a template. Regardless, you should be able to type it fluently (know the steps, t ...