Supervised Learning Applications - II


CSE 891: Deep Learning

Vishnu Boddeti

Wednesday November 08, 2021

Regression: Object Alignment

Regression: Object Alignment

Structured Output Prediction

  • Traditional Learning: Mapping $f : \mathcal{X} \rightarrow \mathbb{R}$
  • Structured Output Learning: Mapping $f : \mathcal{X} \rightarrow \mathcal{Y}$
Semantic Segmentaion

Human Pose Estimation

Pose Estimation: Difficulty

Pose Estimation: Difficulty

Parts Model

Deformable Parts Model

Parts Model

Pose Estimation: Results

Dense Structured Output Prediction

  • Recognizing and delineating objects in an image
  • Classifying each pixel in the image
  • A fully connected graphical model can account for contextual information in the image.

Dense CRF

  • Pairwise energies are defined for every pixel pair in the image.
    • \begin{equation} E(\mathbf{x}) = \sum_{i} unary(x_i) + \sum_{(i,j) \in G} pairwise(x_i,x_j) \nonumber \end{equation}
  • Exact inference is not feasible.
  • Use approximate mean field inference.

Mean Field Approximation

Unrolling Message Passing

Learning Message Passing

3D Computer Vision

3D Representations

Depth Estimation

Depth Estimation Network

  • Eigen, Puhrsh, and Fergus, “Depth Map Prediction from a Single Image using a Multi-Scale Deep Network”, NeurIPS 2014
  • Eigen and Fergus, “Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture”, ICCV 2015
  • Slide Credit: Justin Johnson

Depth Estimation Loss

  • $L_2$ distance is scale variant
    • Absolute scale/depth are ambiguous from a single image
  • We need a scale invariant loss
    • $$ \begin{equation} D(\mathbf{y},\mathbf{y}^*) = \frac{1}{2n^2}\sum_{i,j} \left( (\log y_i - \log y_j) - (\log y^*_i - \log y^*_j) \right)^2 \end{equation} $$

3D Surface Normal Estimation

3D Surface Normal Estimation Network

  • Eigen and Fergus, “Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture”, ICCV 2015
  • Slide Credit: Justin Johnson

Pixel2Mesh

  • Key Ideas:
  • Iterative Refinement
  • Graph Convolution
  • Vertex ALigned Features
  • Chamfer Loss Function
  • Wang et al, "Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images", ECCV 2018

Neural Radiance Fields

  • Mildenhall et al, ”NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis”, ECCV 2020

Motion Deblurring

  • Purohit et al "Region-Adaptive Dense Network for Efficient Motion Deblurring" AAAI 2019
  • Purohit et al "Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring" CVPR 2020

Motion Deblurring Results

  • Purohit et al "Region-Adaptive Dense Network for Efficient Motion Deblurring" AAAI 2019
  • Purohit et al "Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring" CVPR 2020

Video from Single Motion Blurred Image

  • Purohit et al "Bringing Alive Blurred Moments" CVPR 2019

Video from Single Motion Blurred Image

  • Purohit et al "Bringing Alive Blurred Moments" CVPR 2019

Knowledge Distillation: Key Idea

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Knowledge Distillation: Idea Map

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

What Should We Distill?

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

How Does it Work?

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Distilling Final Feature

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Distilling Intermediate Features

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Student Model Design

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Online and Self Distillation

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Adversarial Knowledge Distillation

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Distillation from Teacher Ensemble

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Distillation from Teacher Ensemble

  • Wu et al "Distilled Person Re-identification: Towards a More Scalable System" CVPR 2019

Knowledge for Domain Adaptation

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Knowledge for Synthetic Data

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Knowledge for Synthetic Data

  • Yin et al "Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion" CVPR 2020

Model Quantization

  • Gou et al "Knowledge Distillation: A Survey" Arxiv 2006.05525

Model Quantization

  • Kim et al "QKD: Quantization-aware Knowledge Distillation" Arxiv 1911.12491

Feature Normalized Knowledge Distillation

  • Xu et al "Feature Normalized Knowledge Distillation for Image Classification" ECCV 2020

Knowledge Distialltion for Privacy Preserving Learning

  • Papernot et al "Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data", ICLR 2017

Distilling Relational Knowledge

  • Park et al "Relational Knowledge Distillation" CVPR 2019

Knowledge Distialltion: Black Magic?

  • Yuan et al "Revisiting Knowledge Distillation via Label Smoothing Regularization", CVPR 2020

Label Smoothing




$y_c^{LS} = (1-\alpha)y_c + \alpha\frac{1}{C}$
  • With label smoothing, the model is encouraged to treat each incorrect class as equally probable.
  • Muller et al "When Does Label Smoothing Help?" NeurIPS 2019