Discovery of Latent 3D Keypoints via End-to-End Geometric Reasoning
Explore end-to-end geometric reasoning for latent 3D keypoint discovery, combining keypoint and geometry learning networks to outperform supervised methods in semi-supervised keypoint finding.
Deep Network for Integrated 3D Sensing of Multiple People in Natural Images
Explore advanced techniques for 3D human pose estimation and shape reconstruction in natural images using deep learning and computer vision approaches.
Taskonomy - Disentangling Task Transfer Learning
Explore task transfer learning, higher-order transfers, and task-specific networks. Discover global taxonomies, evaluate computed structures, and assess generalization to novel tasks.
VideoCapsuleNet - A Simplified Network for Action Detection
Explore VideoCapsuleNet, a simplified network for action detection in videos. Learn about capsule networks, convolutional layers, and innovative techniques for improved video analysis.
Neural-Symbolic VQA - Disentangling Reasoning from Vision and Language Understanding
Explore neural-symbolic visual question answering, disentangling reasoning from vision and language understanding through task breakdown, architecture overview, and practical applications.
Adaptive Attacks to Adversarial Example Defenses - CAP6412 Spring 2021
Explore adaptive attacks on adversarial example defenses, covering expectation over transformation, K winners take all, chaos partitioning, and local gradient estimation.
Improving Transferability of Adversarial Examples with Input Diversity - CAP6412 Spring 2021
Explore techniques to enhance adversarial example transferability using input diversity, including FGSM family methods and ensemble network attacks.
Universal Adversarial Perturbations in Machine Learning - Spring 2021
Explore universal adversarial perturbations in machine learning, covering definitions, contributions, experiments, and cross-model universality.
Explaining and Harnessing Adversarial Examples in Machine Learning - Spring 2021
Explore adversarial examples in machine learning, their impact on linear and non-linear models, and techniques like adversarial training to improve model robustness and generalization.
Intriguing Properties of Neural Networks - Spring 2021
Explore intriguing properties of neural networks, including adversarial attacks, transfer, training overlap, and semantic information in units. Analyze weaknesses and spectral instability.
Introduction to Adversarial Attacks in Machine Learning - Lecture 1
Introduction to adversarial attacks on AI systems, covering real-world examples, attack methods, and defense strategies for various applications like face recognition and object detection.
Fast Is Better Than Free: Revisiting Adversarial Training - CAP6412 Spring 2021
Explore adversarial training techniques, comparing PGD with newer methods. Learn about technical contributions, experimental results, and implications for machine learning security.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Explore techniques for interpreting and explaining deep learning models, focusing on LRP rules, Taylor decomposition, and properties of effective explanations in AI systems.
CAP6412 - SmoothGrad: Removing Noise by Adding Noise - Lecture
Explore SmoothGrad, a technique for enhancing neural network visualization by adding noise to input images and averaging resulting sensitivity maps.
Interpretable Explanations of Black Boxes by Meaningful Perturbation - CAP6412 Spring 2021
Explore principles and methods for explaining black box functions in machine learning, focusing on image saliency, perturbation techniques, and meta-predictors for interpretable AI.
Unmasking Clever Hans Predictors and Assessing Machine Learning - Spring 2021
Unmasking clever predictors: Explore Layer-wise Relevance Propagation and SPRAY techniques to assess machine learning models' decision-making processes and identify potential biases or faults.
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.
Adversarial Self-Supervised Contrastive Learning
Explore adversarial self-supervised contrastive learning, covering transformers, frameworks, algorithms, evaluations, and experiments in this comprehensive presentation.
Contrastive Learning with Adversarial Examples - Spring 2021
Explore contrastive learning with adversarial examples, covering unsupervised embedding, self-supervised learning, experiments, and architectural studies in this comprehensive overview.
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.
TinyAction Challenge - Action Recognition in Short Videos
Explore cutting-edge research on tiny action recognition, including data analysis, evaluation methods, and experimental results in computer vision and AI.