Zamir A R, Sax A, Shen W, et al. Taskonomy: Disentangling Task Transfer Learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 3712-3722.

# 1. Overview

## 1.1. Motivation

- relationships among different visual tasks

In this paper

- proposes a fully computational approach for
**modeling the structure of space of visual task** - exploit it to reduce the demand for labeled data

## 1.2. Related Works

- self-supervised learning
- unsupervised learning
- meta-learning
- multi-task learning
- domain adaption

# 2. Methods

## 2.1. Definitions

**γ**. limited supervision budget**T (target)**. set of task want to solve**S (source)**. set of task can be trained**V=T∪S**. task dictionary**T∩S**. task want to solve but can play as source**T-T∩S**. task can not trained (target only)**S-T∩S**. (source only)**edge**. between a group of source and target tasks, represent feasible case**weight**. prediction of its performance- use these edges to estimate the
**globally optimal transfer policy**to solve T

**Four Steps**

## 2.2. Stage I: Task-Specific Modeling

- Encoder-Decoder

## 2.3. Stage II: Transfer Modeling

learn a readout function

**E_s**. encoder**D_Θ**. readout function**f_t**. gt of task t for image I.- the performance of
**D_{s→ t}**is a useful metric as task affinity - use shallow fully convolutional network
**combinatorial explosion of higher-oder transfer.**using beam search to sample

## 2.4. Stage III: Ordinal Normalization using Analytic Hierarchy Process (AHP)

- aggregating the raw losses/evaluations L_{s→ t} from transfer function into a matrix is problem. vastly different scale and live in different spaces
- naive solution of linearly rescale each fail

## 2.5. Step IV: Computing the Global Texonomy

edge (hypergraph)

Boolean Integer Programming (BIP) to find global policy

## 2.6. Constrains

- each target task has exactly one transfer in
- supervision budget is not exceeded