Segmentation: Techniques and Applications

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Related lectures (31)

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Explores second-order model compression for massive deep neural networks, showcasing compression techniques and their impact on model accuracy.

Fourier Transform: Compression and Analysis

Covers the Fourier transform, compression techniques, and spectral analysis of periodic signals.

PCA: Directions of Largest Variance

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Video Compression: Standards & Techniques

Explores the evolution of video compression standards and techniques, from H.261 to VVC, emphasizing advancements in compression efficiency and video quality.

Uncertainty Quantification and Label Error Detection

Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.

Mohr Circle: Stress Analysis

Explains stress analysis using Mohr circles and strain energy concepts.

Data Compression and Entropy: Basics and Introduction

Introduces data compression, entropy, and the importance of reducing redundancy in data.

Document Segmentation: dhSegment

Explores the development and improvements of dhSegment, an open-source document segmentation package using PyTorch.

Model Compression: Techniques for Efficient NLP Models

Explores model compression techniques in NLP, discussing pruning, quantization, weight factorization, knowledge distillation, and attention mechanisms.

Efficient Storage for Analytics: Columnar Storage & Compression Techniques

Explores columnar storage, compression techniques, DSM properties, and PAX advantages for analytics.