Source-linked AI summary
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo
TL;DR
Limited labeled data motivates unsupervised representation learning. AET addresses this by predicting randomly sampled transformations from encoded original and transformed images rather than reconstructing data. The paper reports substantial improvements over state-of-the-art unsupervised methods, narrowing the gap with fully supervised counterparts.
Problem
Deep neural networks often require large amounts of labeled data, motivating unsupervised representation learning when labeled examples are insufficient.
Method
AET jointly learns an encoder and decoder that estimate randomly sampled transformations from encoded representations of original and transformed images.
Results
AET substantially improves over state-of-the-art unsupervised methods and significantly narrows the performance gap with fully supervised counterparts across CIFAR-10, ImageNet, and Places.
Takeaways & Limitations
AET supports a wide spectrum of transformations for unsupervised representation learning, including parameterized, GAN-induced, and non-parameterized forms.
Takeaways & Limitations
The paper focuses on parameterized transformations because GAN-induced methods depend on generated-image quality and non-parameterized methods require auxiliary approximations.
Abstract
from arXiv · showhide
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.
1. Introduction
Limited labeled data motivates unsupervised representation learning. The paper proposes AET, which predicts image transformations from encoded features rather than reconstructing input data, and reports stronger performance than existing unsupervised methods.
- Deep neural networks often require extensive labeled data, limiting their applicability when labeled examples are scarce.
- Conventional auto-encoders learn representations by reconstructing input images with a paired decoder, forming the AED paradigm.
- AET trains auto-encoders to reconstruct randomly sampled image transformations from features of original and transformed images.
- Unlike AED, AET explores how feature representations change under transformations and supports parameterized, non-parameterized, and GAN-induced transformations.
- AET representations are reported to outperform other unsupervised models and narrow the gap with fully supervised counterparts.
2. Related Work
Related work includes auto-encoders and GANs as unsupervised representation-learning paradigms. Auto-encoders reconstruct data, whereas GANs generate data from random noise that can serve as a representation.
- Auto-Encoders: Auto-encoders learn compact feature representations by encoding inputs and reconstructing them through a jointly trained decoder.
- Generative Adversarial Nets: GANs generate data from random noise, which can be viewed as coordinates or features representing points on the real-data manifold.
3. AET: The Proposed Approach
AET jointly trains an encoder and decoder to estimate sampled transformations from representations of original and transformed images. The framework accommodates several transformation families, while the experiments focus on parameterized transformations because of model-quality and comparison constraints.
- Formulation: AET samples transformations and applies each to an image, producing original and transformed inputs for representation learning.
- Formulation: The encoder maps images to representations, while the decoder estimates the input transformation from the two encoded representations.
- Formulation: Training jointly updates the encoder and decoder by minimizing a loss measuring the difference between sampled transformations and their estimates.The loss is optimized over sampled transformations and data using mini-batch backpropagation.
- The AET Family: AET can instantiate parameterized, GAN-induced, and non-parameterized transformations, including geometric and semantic image changes.Projective transformations use parameterized matrices, while GAN-induced transformations can alter attributes such as age, hair, gender, glasses, or furniture layout.
- The AET Family: For non-parameterized transformations, AET estimates a parameterized approximation because the objective is representation learning rather than exact transformation recovery.
- The AET Family: Parameterized transformations are trained with parameter-based loss because image-distance loss may not accurately reflect transformation differences without many sampled images.
- The AET Family: Experiments focus on parameterized transformations to avoid extra GAN training, auxiliary approximations, and dependence on insufficiently high-quality generated images.
4. Experiments
Experiments evaluate AET on CIFAR-10 and ImageNet against unsupervised and supervised references using multiple classifier protocols. AET consistently outperforms compared unsupervised methods and narrows the gap to fully supervised performance.
- Evaluation setup: AET is evaluated on CIFAR-10, ImageNet, and Places using established indirect protocols that assess learned representations through classification.The supplied experiment setup specifies CIFAR-10, ImageNet, and Places evaluations, while the detailed methods here cover CIFAR-10 and ImageNet comparisons.
- CIFAR-10 results: On CIFAR-10, unsupervised AET-project reaches a 7.82% error rate versus 7.2% for the fully supervised NIN counterpart.The comparison uses a convolutional classifier and reports that AET-project nearly matches the supervised reference.
- CIFAR-10 results: AET outperforms other unsupervised CIFAR-10 methods, including ExamplarCNN, indicating more effective use of image transformations during unsupervised training.ExamplarCNN also applies transformations, but the paper reports that AET maintains a significant error-rate lead.
- CIFAR-10 results: Using RotNet as a matched baseline, AET beats it with fully connected and convolutional classifiers and achieves the smallest errors across tested classifiers.The paper also reports an advantage in model-free KNN evaluation when no labels are used to train the classifier.
- ImageNet results: On ImageNet, AET greatly outperforms other unsupervised methods and narrows the upper-bound Top-1 accuracy gap relative to RotNet and DeepCluster.The reported gaps for RotNet and DeepCluster are 9.7% and 15.7%, respectively, before AET’s reduction.
- ImageNet results: AET obtains the best accuracy among compared unsupervised models with both nonlinear and linear ImageNet evaluation protocols.The nonlinear comparison uses classifiers on Conv4 and Conv5, while the linear protocol trains a 1,000-way classifier on resized feature maps.
- Analysis: Transformation-prediction loss trends align with classification error and Top-1 accuracy on CIFAR-10 and ImageNet.This relation connects the AET training objective to downstream classification performance in the reported experiments.
5. Conclusions
The paper introduces AET as an unsupervised representation-learning paradigm that predicts randomly sampled transformations instead of reconstructing data. Experiments show substantial improvements over state-of-the-art unsupervised methods while narrowing the gap with fully supervised counterparts.
- AET trains unsupervised representations by estimating randomly sampled transformations at the output rather than reconstructing input data.
- AET encodes visual information from both original and transformed images so their transformations can be predicted.
- AET accommodates a wide variety of transformations within its unsupervised training framework.
- AET substantially improves over state-of-the-art unsupervised methods and significantly narrows the performance gap with fully supervised counterparts.