Add layers during training Net to Net [?] Ian Goodfellow Network Morphism Microsoft Deep Visual-Semantic: Alignments for Generating Image Descriptions Types of RNN - Vanilla RNN / Simple RNN / Elman RNN - Long Short Term Memory (LSTM) - Helps improve gradient flow during back prop Computer Vision Tasks - Classification [already done] - Very basic - Localization - Segmentation - Detection 1. Semantic Segmentation - Grass, Cat, Tree, Sky in 1 image. [No object just pixels] - No box but all pixels are classified. 2. Classification + Localization - Class = Cat and a box around cat [Single Object] 3. Object detection - Box around objects - Dog, Dog, Cat 4. Instance Segmentation - All pixels are classified - Dog Cat Dog 3 and 4 - Multiple objects. Semantic Segmentation - For every pixel we want to say what it is - cat, grass, sky, etc - Two Cows will be classified as cow, both are not differentiated like cow 1 and cow 2. - Instance segmentation does this. - Just labeling the