How Can I Explain This to You? An Empirical Study of Deep Neural Net Explanation Methods - Spring 2021
Empirical study comparing deep neural network explanation methods, examining usability, stability, and privacy risks across various visual explanation frameworks.
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior - Spring 2021
Explore video adversarial attacks using the Motion-Excited Sampler technique, covering problem formulation, methodology, gradient estimation, and results analysis.
Video Content Understanding Using Text
Explore video content understanding using text, covering challenges, algorithms, and techniques for video representation, scoring, optimization, and inaccuracy detection.
Action Recognition and Video Classification Techniques - Lecture 19
Learn about action recognition in videos, including 3D convolution, video classification, and recurrent neural networks like LSTM, with practical aspects and applications to datasets like UCF101 and VIRAT.
Computer Vision: Classification Techniques - Neural Networks and CNN Architecture - Lecture 12
Explore machine learning frameworks, neural networks, and CNN architecture, focusing on AlexNet and fully convolutional networks. Learn about activation functions and network composition.
Autoencoder Architecture and Applications - Lecture 10
Explore autoencoders: architecture, comparison, applications in image retrieval, denoising, and anomaly detection. Gain insights into this powerful neural network technique.
Image Filtering, Convolution, and Edge Detection
Explore image processing techniques including filtering, convolution, and edge detection. Learn about derivatives, noise reduction, and various edge detection algorithms for computer vision applications.
CAP5415 - Introduction to Convolutional Neural Networks Part 2 - Lecture 6
Explore convolutional neural networks' architecture, layers, and features. Learn about AlexNet, pooling, normalization, and the progression from low-level to complex features in deep learning.
Introduction to Convolutional Neural Networks - Part 1 - Lecture 6
Comprehensive introduction to Convolutional Neural Networks, covering history, architecture, key concepts, and applications in image processing and beyond.
Textual Explanation for Self-Driving Vehicles
Explore self-driving vehicle technology through an explainable driving model, focusing on network architecture, preprocessing, and evaluation of vehicle control and explanation generation.
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
Learn to automatically remove objects from images using weak supervision, exploring mask generation and image inpainting techniques for seamless scene editing.
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
Learn about unpaired image-to-image translation using cycle-consistent adversarial networks, exploring challenges, intuition, loss functions, and results in this comprehensive overview.
Understanding Deep Learning Requires Rethinking Generalization
Explore deep learning generalization, examining regularization techniques, randomization tests, and neural network expressivity to challenge conventional understanding and improve model performance.
Pixel Recurrent Neural Networks
Explore cutting-edge image generation techniques, focusing on Pixel Recurrent Neural Networks. Learn about model architectures, RNN concepts, and LSTM equations for advanced AI applications.
Deep Reinforcement Learning for Object Tracking
Explore action-driven object tracking using deep reinforcement learning, covering policy, value functions, and training approaches for improved visual tracking performance.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Explore high-resolution image synthesis and semantic manipulation using conditional GANs, covering advanced techniques like coarse-to-fine generation and multi-scale discriminators for improved photorealism.
What If We Do Not Have Multiple Videos of the Same Action - Video Action Localization
Explore video action localization techniques for scenarios with limited action samples, covering key challenges, innovative approaches, and experimental results.
Filtering in Computer Vision - Lecture 2
Learn about image filtering techniques, including noise reduction, derivatives, convolution, and Gaussian filters. Explore practical applications in computer vision using MATLAB.
Computer Vision Architecture Evolution: ConvNets to Transformers - Lecture 21
Explore the evolution of vision architectures, comparing SWIN and CNNs, and learn about modernizing ConvNets with macro and micro design changes for improved performance.
Rethinking and Improving Relative Position Encoding for Vision Transformer - Lecture 23
Explore vision transformer improvements through relative position encoding, examining bias modes, piecewise indexing, and 2D calculations for enhanced performance in image processing tasks.
Computer Vision - Adversarial Attacks and Deepfool Algorithm - Lecture 1
Explore DeepFool algorithm for generating adversarial examples in deep neural networks, covering linearization, multiclass classification, and experimental results.