The project website for "Auto-Encoding Transformations."
The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios. To address this challenge, unsupervised methods are strongly preferred for training neural networks without using any labeled data. In this paper, we present a novel paradigm of unsupervised representation learning by Auto-Encoding Transformation (AET) in contrast to the conventional Auto-Encoding Data (AED) approach. Given a randomly sampled transformation, AET seeks to predict it merely from the encoded features as accurately as possible at the output end. The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of original and transformed images, the transformation can be well predicted. We will show that this AET paradigm allows us to instantiate a large variety of transformations, from parameterized, to non-parameterized and GAN-induced ones. Our experiments show that AET greatly improves over existing unsupervised approaches, setting new state-of-the-art performances being greatly closer to the upper bounds by their fully supervised counterparts on CIFAR-10, ImageNet and Places datasets.
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| (a) Auto-Encoding Data |
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| (b) Auto-Encoding Transformation |
| Figure 1. An illustration of the comparison betweeen AED and AET models. AET attempts to estimate the input transformation rather than the data at the output end. This forces the encoder network E to extract the features that contain the sufficient information about visual structures to decode the input transformation. |
Figure 1 illustrates our idea of auto-encoding transformation (AET) in comparison with the conventional auto-encoding data (AED). We build a transformation decoder D to reconstruct the input transformation t from the representations of an original image E(x) and the transformed image E(t(x)), where E is the representation encoder.
The least-square difference between the estimated transformation and the original transformation is minimized to train D and E jointly. For details, please refer to our paper.
- Python == 2.7
- pytorch == 1.0.1
- torchvision == 0.2.1
- PIL == 5.4.1
Please use the torchvision with version 0.2.1. The code does not support the newest version of torchvision.
cd cifar/affine
or
cd cifar/projective
Unsupervised learning:
CUDA_VISIBLE_DEVICES=0 python main.py --cuda --outf ./output --dataroot $YOUR_CIFAR10_PATH$
Supervised evaluation with two FC layers:
python classification.py --dataroot $YOUR_CIFAR10_PATH$ --epochs 200 --schedule 100 150 --gamma 0.1 -c ./output_cls --net ./output/net_epoch_1499.pth --gpu-id 0
cd imagenet
Generate and save 0.5 million projective transformation parameters:
python save_homography.py
Unsupervised learning:
CUDA_VISIBLE_DEVICES=0 python main.py --exp ImageNet_Unsupervised
Supervised evaluation with non-linear classifiers:
CUDA_VISIBLE_DEVICES=0 python main.py --exp ImageNet_NonLinearClassifiers
Supervised evaluation with linear classifiers (max pooling):
CUDA_VISIBLE_DEVICES=0 python main.py --exp ImageNet_LinearClassifiers_Maxpooling
Supervised evaluation with linear classifiers (average pooling):
CUDA_VISIBLE_DEVICES=0 python main.py --exp ImageNet_LinearClassifiers_Avgpooling
To use the pretrained ImageNet model:
mkdir experiments
cd experiments
mkdir ImageNet_Unsupervised
Please download the pre-trained model from the link: https://1drv.ms/u/s!AhnMU9glhsl-xxI-e68xrOe3gvQg?e=nFNnir and put the models under ./experiments/ImageNet_Unsupervised
Firstly pretrain the model on Imagenet, then evalutate the model with linear classifiers (max pooling):
CUDA_VISIBLE_DEVICES=0 python main.py --exp Places205_LinearClassifiers_Maxpooling
Supervised evalutation with linear classifiers (average pooling):
CUDA_VISIBLE_DEVICES=0 python main.py --exp Places205_LinearClassifiers_Avgpooling
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo. AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, June 16th - June 20th, 2019. [pdf]
Some of our codes reuse the github project FeatureLearningRotNet.
This code is released under the MIT License.