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# System prepended metadata

title: Constrative Embedding for Generalized Zero-Shot Learning

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# Constrative Embedding for Generalized Zero-Shot Learning

## Term explanations

::: spoiler 

Semantic description
	: The concept of semantic information refers to information which is in some sense meaningful for a system


Zero-Shot Learning
	: A learning method that first trained by datasets provided, Relying on the semantic descriptions, such as **Visual attributes or Word Vector**, transfering knowledges to recognize unseen class in a data-free manner.

Long-Tail distribution
	: Some categories have abundant samples while some categories have few or even no sampls
	![](https://i.imgur.com/LptUS3j.png)
	
Class-wise supervision
	: Recognizing the target with respect to classes, which means it's possible to misrecognize instances in the same class which might not be 100% similiar to the semantic description
	
Instance-wise supervision
	: Recognizing the target with respect to instance, which means it's able the tolerant differences between instances in the same class, given a more general recogniztion.
	
	
Commonly-used Semantic Embedding Model
	:	Embed the feature by **ranking loss**, requires the correct semantic descriptor to be ranked higher than wrong descriptors.
	**Only utilize class-wise supervision.**
	
Contrastive Embedding Model
	:	Learns to discriminate a positive sample and large amount of negative sample.
		When embedding two data, with same class but different instance, they should still keep similar after embedding.
		
:::success
:notes: When using ranking loss, result comparing two different instance in the same class will be negative, while contrastive loss will think they're similiar

:::

## Abstract

GZSL problem aims to recognize classes that is unseen when training, comparing to conventional ZSL problem, it's way more hard, since GZSL suffers from a tighter constraint.

Solutions in the past are also suffering from some problem, making them only suboptimal, which will be mentioned in **Introduction**

The author provide a brand new framework which try to solve this sort of problem.
Also they try to make the model more general, utilizing not only class-wise supervision but also instance-wise supervision.

## Introduction

In conventional-ZSL problem,we assumed that **only unseen** data will appear in testing datasets, but when we are generalizing the problem, in Generalized-ZSL problem, **not only unseed but also seen** labels appears in testing dataset, which causes severe biased problem when using conventional-ZSL method.

Usaully, people solve GZSL problem by Feature generation-based model, merging **synthesis** unseen data with provoded data yield the whole training datasets. 
:::danger
Since we generate synthesis unseen data in the original feature space, we conjecture our origin feature space, far from the semantic description, it's still a suboptimal problem, and thus lack of **discriminative ability**
:::

To deal with this suboptimal dilemma, the author provide a novel framework.

By mixing a Embedding model and a Feature Generation-Based model, their model can solve the above-mentioned problem by mapping both provided data and synthesis unseen data to a new feature space.

Also by using a contrastive embedding model, the model can be more general, utilizing both class-wise supervision but also instance-wise supervision.