Quiz 4 Topic List (tentative)

Introduction to Recognition

Machine Learning Basics

1. Nearest neighbors

2. Support Vector Machines

3. Support Vector Machines with Kernels

4. Bias variance trade-off

5. Model Selection

Feature Engineering

1. Digit Classification Case Study

Deep Neural Networks

1. Back-propagation

2. Development Cycle

3. Hyper-parameters and their affect

Image Classification

1. Architectural Constructs and their Properties

   1. Convolutions

   2. Pooling

   3. Fully-connected Layers

   4. Non-linearities

   5. Dropout

   6. Batch-normalization

   7. Residual Connections

   8. Attention

2. Popular Architectures

3. Using ImageNet pre-trained models for vision tasks

Segmentation

1. Tasks and Metrics

   1. Bottom-up segmentation

   2. Semantic segmentation

   3. Instance segmentation

2. Shift and Stitch, Dilated Convolutions

3. Skip-connections

4. Encoder-decoder architectures

Detection

1. Problem definition

2. Metrics

3. Basics of classical approaches

4. CNN based Detectors

   1. R-CNN

   2. Fast R-CNN

   3. Faster R-CNN

   4. Single-shot Detectors

   5. Feature Pyramid Networks

5. ROI Pooling

6. Non-Maximum Suppression

7. Region Proposal Networks

8. Instance Segmentation

   1. Metrics

   2. RoI Align vs RoI Pooling