# saas

**Repository Path**: BolinLi-s/saas

## Basic Information

- **Project Name**: saas
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-25
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


This repository implements the SaaS algorithm proposed in [[1]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Safa_Cicek_SaaS_Speed_as_ECCV_2018_paper.pdf) using PyTorch.

### SaaS

First phase of the SaaS: To get the estimates on unlabeled data, run either of the following:

```python saas_nozca.py --dataset 'svhn' --net_name resnet ```

```python saas_nozca.py --dataset 'cifar10' --net_name resnet ```

Second phase of the SaaS: train on labeled data augmented with the estimates of unlabeled data from the first phase. This phase is simply a supervised learning. 

```python saas_sup_nozca.py --dataset 'svhn' --net_name resnet```

```python saas_sup_nozca.py --dataset 'cifar10' --net_name resnet ```

### Fig-1

The minimal code to reproduce the figure-1 in the paper SaaS is inside the folder fig1.

## Reference
[1] S. Cicek, A. Fawzi and S. Soatto: *SaaS: Speed as a Supervisor for Semi-supervised Learning*.  In Proceedings of the European Conference on Computer Vision (ECCV ’18).