# Brief Review — Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

## PBA, Starts by **Easier Augmentation, Followed by Harder Augmentation**

@ Medium)

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules, PBA, by UC Berkeley, (Unknown named X), and covariant.ai2019 ICML, Over 200 Citations(

Data Augmentation, Image Classification

**Population Based Augmentation (PBA)**is proposed to**learn a schedule of augmentation policies.**

# Outline

**Population Based Augmentation (PBA)****Results**

# 1. Population Based Augmentation (PBA)

## 1.1. Learning a Schedule

**Population Based Training (PBT)**is leveraged:**A hyperparameter search algorithm**which optimizes the parameters of a network jointly with their hyperparameters to maximize performance.- The
**output**of PBT is not an optimal hyperparameter configuration**a trained model****and schedule of hyperparameters**.

Similarly, in PBA, we are

only interested in the learned scheduleanddiscard the child model result.

- In
**PBT**, to start, a**fixed population of models**are randomly initialized and**trained in parallel**. At certain intervals, an**“exploit-and-explore”**procedure is applied.

For the

worse models,the model clones the weights of a better performing model (i.e., exploitation)and thenperturbs the hyperparametersof the cloned model to search in the hyperparameter space(i.e., exploration).

## 1.2. Policy Search Space (Algorithm 1)

- A set of hyperparameters consists of two
**magnitude**and**probability**values for**each operation**. - This gives us
**30 operation-magnitude-probability tuples**for**a total of 60 hyperparameters**. - Similar to AutoAugment, there are
**10 possibilities**for**magnitude**and**11 possibilities**for**probability**. - When augmentations are applied to data, all operations are first
**shuffled**and then**applied**in turn**until a limit is reached**. This limit can range**from 0 to 2 operations**, as shown in**Algorithm 1**above.

- AutoAugment uses RNN controller to return the hyperparameters to be used, similar to NASNet.

PBA search spaceincludes (10×11)³⁰≈1.75×10⁶¹ possibilities, compared to2.8×10³² forAutoAugment.

## 1.3. Training Flow

- In each iteration we run an epoch of gradient descent.
- A trial is evaluated on a validation set not used for PBT training and disjoint from the final test set.
- A trial is ready to go through the exploit-and-explore process once 3 steps/epochs have elapsed.
**Exploit**:**Truncation Selection**. as in PBT, is used, where a trial in the**bottom 25% of the population clones the weights and hyperparameters of a model in the top 25%**.**Explore**: For each hyperparameter, PBA either**uniformly resamples from all possible values**or**perturbs the original value**, as shown in**Algorithm 2**.- In experiment, PBA is run with
**16 total trials**on the**Wide-ResNet****-40–2**model to generate augmentation schedules.

# 2. Results

- Overall, the PBA learned schedule leads AutoAugment slightly on PyramidNet and Wide-ResNet-28–10, and performs comparably on Shake-Shake models, showing that the learned schedule is competitive with state-of-the-art.

- Training with the PBA Fixed Policy degrades accuracy by 10% percent on average,

It is hypothesized that schedule improves training by allowing “**easy” augmentations** in the **initial phase** of training while still allowing **“harder”** **augmentations **to be **added later on**.

## Reference

[2019 ICML] [PBA]

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

**1.1. Image Classification**

**1989 … 2019** [PBA] **… 2022 **[ConvNeXt] [PVTv2] [ViT-G] [AS-MLP]