Learning to Teach: Difference between revisions

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=Introduction=
=Introduction=
Object tracking has been a hot topic in recent years. It involves localization of an object in continuous video frames given an initial annotation in the first frame.
In modern human society, the role of teaching is heavily implicated in our education system, the goal being to equip students with necessary knowledge and skills in an efficient manner. This is the fundamental ''student'' and  ''teacher'' framework on which education stands. However, in the field of artificial intelligence and specifically machine learning, researchers have focused most of their efforts on the ''student'' ie. designing various optimization algorithms to enhance the learning ability of intelligent agents. The paper argues that a formal study on the role of ‘teaching’ in AI is required. Analogous to teaching in human society, the teaching framework can select training data which corresponds to choosing the right teaching materials (e.g. textbooks); designing the loss functions corresponding to setting up targeted examinations; defining the hypothesis space corresponds to imparting the proper methodologies. Furthermore, an optimization framework (instead of heuristics) should be used to update the teaching skills based on the feedback from students, so as to achieve teacher-student co-evolution.
The process normally consists of the following steps.  
<ol>
<li> Taking an initial set of object detections. </li>
<li> Creating and assigning a unique ID for each of the initial detections. </li>
<li> Tracking those objects as they move around in the video frames, maintaining the assignment of unique IDs. </li>
</ol>
There are two types of object tracking. <ol> <li>Passive tracking</li> <li> Active tracking </li> </ol>


Passive tracking assumes that the object of interest is always in the image scene, meaning that there is no need for camera control during tracking. Although passive tracking is very useful and well-researched with existing works, it is not applicable in situations like tracking performed by a camera-mounted mobile robot or by a drone. 
This paper proposed the "learning to teach" (L2T) framework with two intelligent agents: a student model/agent, corresponding to the learner in traditional machine learning algorithms, and a teacher model/agent, determining the appropriate data, loss function, and hypothesis space to facilitate the learning of the student model.
On the other hand, active tracking involves two subtasks, including 1) Object Tracking and 2) Camera Control. It is difficult to jointly tune the pipeline between these two separate subtasks. Object Tracking may require human efforts for bounding box labeling. In addition, Camera Control is non-trivial, which can lead to many expensive trial-and-errors in the real world.


=Intuition=
=Related Work=
 
The L2T framework connects with two trends in machine learning.
As in the case of the state of the art models, if the action module and the object tracking module are completely different, it is extremely difficult to train one or the other as it is impossible to know which is causing the error that is being observed at the end of the episode. The function of both these modules are the same at a high level as both are aiming for efficient navigation. So it makes sense to have a joint module that consists of both the observation and the action taking submodules. Now we can train the entire system together as the error needs to be propagated to the whole system. This is in line with the common practice in Deep Reinforcement Learning where the CNNs used to extract features in the case of Atari games are combined with the Q networks (in the case of DQN). The training of these CNN happens concurrently with the Q feedforward networks where the error function is the difference between the observed Q value and the target Q values.

Revision as of 18:23, 31 October 2018

Introduction

In modern human society, the role of teaching is heavily implicated in our education system, the goal being to equip students with necessary knowledge and skills in an efficient manner. This is the fundamental student and teacher framework on which education stands. However, in the field of artificial intelligence and specifically machine learning, researchers have focused most of their efforts on the student ie. designing various optimization algorithms to enhance the learning ability of intelligent agents. The paper argues that a formal study on the role of ‘teaching’ in AI is required. Analogous to teaching in human society, the teaching framework can select training data which corresponds to choosing the right teaching materials (e.g. textbooks); designing the loss functions corresponding to setting up targeted examinations; defining the hypothesis space corresponds to imparting the proper methodologies. Furthermore, an optimization framework (instead of heuristics) should be used to update the teaching skills based on the feedback from students, so as to achieve teacher-student co-evolution.

This paper proposed the "learning to teach" (L2T) framework with two intelligent agents: a student model/agent, corresponding to the learner in traditional machine learning algorithms, and a teacher model/agent, determining the appropriate data, loss function, and hypothesis space to facilitate the learning of the student model.

Related Work

The L2T framework connects with two trends in machine learning.

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