Skip to content

Linked TOC to in-page references #1

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Linked TOC to in-page references #1

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 16 additions & 15 deletions readme.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,21 +2,22 @@

## Table of Contents

* Few Shot Learning Literature
* General Setup and Datasets
* Siamese and Triplet Networks
* Cross Entropy Loss
* Contrastive Loss
* Triplet Loss
* Matching Networks
* Meta-Agnostic Model Learning
* View-Manifold Learning
* Prototypical Networks
* Activations to Parameters
* Weight Imprinting
* Human Robot Interaction Setting
* Differences to Few Shot Literature
* KUKA Innovation Challenge
* [Few Shot Learning Literature](#1-few-shot-learning-literature)
* [General Setup and Datasets](#11-general-setup-and-datasets)
* [Siamese and Triplet Networks](#12-siamese-and-triplet-networks)
* [Cross Entropy Loss](#121-cross-entropy-loss)
* [Contrastive Loss](#122-contrastive-loss)
* [Triplet Loss](#123-triplet-loss)
* [Matching Networks](#14-matching-networks)
* [Meta-Agnostic Model Learning](#16-maml)
* [View-Manifold Learning](#13-view-manifold-learning)
* [Prototypical Networks](#15-prototypical-networks)
* [Activations to Parameters](#17-activations-to-parameters)
* [Weight Imprinting ](#18-weights-imprinting)
* [Human Robot Interaction Setting](#2-hri-setting)
* [Differences to Few Shot Literature](#21-differences-to-few-shot-learning-literature)
* [KUKA Innovation Challenge](#22-kuka-innovation-challenge)
* [References](#references)


Few Shot Learning, the ability to learn from few labeled samples, is a vital step in robot manipulation. In order for robots to operate in dynamic and unstructured environments, they need to learn novel objects on the fly from few samples. The current object recognition methods using convolutional networks are based on supervised learning with large-scale datasets such as ImageNet, with hundreds or thousands labeled examples. However, even with large-scale datasets they remain limited in multiple aspects, not all objects in our lives are within the 1000 labels provided in ImageNet.
Expand Down