Unlocking the Power of Unlabeled Data: A Comprehensive Guide to Deep Learning Algorithms – Self-training Demystified (Part 1)
Introduction:
Deep models require a large amount of labeled training data, which can be difficult to obtain. As a result, researchers have turned to leveraging unlabeled data, which is more readily available. For example, crawling the web can provide large quantities of unlabeled image data. Recent developments have shown that models trained with unlabeled data can achieve performance comparable to fully-supervised models. In this blog series, we will discuss our theoretical analysis of empirical methods that use unlabeled data. In this first post, we will analyze the self-training algorithm, a powerful paradigm for semi-supervised learning and domain adaptation. The effectiveness of self-training is surprising, given that it retrains on pseudo-labels rather than true labels. However, our theoretical analysis will show that self-training can indeed improve accuracy. We will also explore the importance of regularization in the self-training process and introduce the concept of the augmentation graph, which allows us to formalize the input consistency regularizer. This regularizer, along with appropriate assumptions about the data, enables self-training to achieve provable improvements. Stay tuned for our next post, where we will analyze self-supervised contrastive learning algorithms.
Full Article: Unlocking the Power of Unlabeled Data: A Comprehensive Guide to Deep Learning Algorithms – Self-training Demystified (Part 1)
Deep models require a lot of training examples, but obtaining labeled data is a challenging task. To overcome this issue, researchers have turned to leveraging unlabeled data, which is more readily available. For example, web crawling can yield large quantities of unlabeled image data, while labeled datasets like ImageNet require expensive labeling processes. Recent empirical developments have shown that models trained with unlabeled data can achieve performance comparable to fully-supervised models.
In this series of blog posts, we will analyze theoretical works that aim to analyze the effectiveness of recent empirical methods that utilize unlabeled data. This first post focuses on self-training, which is an algorithmic paradigm for semi-supervised learning and domain adaptation. In Part 2, we will explore self-supervised contrastive learning algorithms, which have been successful in unsupervised representation learning.
Background: Self-training
Self-training algorithms are the main focus of this blog post. The core idea is to use an existing classifier, referred to as the “pseudo-labeler” ((F_{pl})), to make predictions (pseudo-labels) on a large unlabeled dataset. A new model ((F)) is then trained from scratch using the pseudo-labels. In semi-supervised learning, the pseudo-labeler is trained on a small labeled dataset and used to predict pseudo-labels on a larger unlabeled dataset. The retraining phase aims to improve the accuracy of (F) compared to (F_{pl}). Surprisingly, self-training has been shown to work well in practice despite retraining on pseudo-labels rather than true labels.
The Importance of Regularization for Self-training
Before delving into the theoretical analysis of self-training, it is important to highlight the necessity of regularization during the retraining phase. Without regularization, the unregularized cross-entropy loss can be driven to zero by scaling up the predictions of (F_{pl}) to infinity. This results in no improvement over (F_{pl}) as the decision boundary remains unchanged. Empirically, input consistency regularization techniques that encourage consistent predictions on neighboring pairs of examples have been effective. These techniques define “neighboring pairs” in various ways, such as examples close in (ell_2) distance or examples with different strong data augmentations of the same image.
Key Formulations for Theoretical Analysis
To understand the effectiveness of self-training, a principled approach to the regularizer is needed. Input consistency regularization, which is effective in practice, needs to be abstracted for tractable analysis. The augmentation graph, introduced in the following section, provides a solution to these challenges.
Augmentation Graph on the Population Data
The augmentation graph is a key concept that formalizes the input consistency regularizer and motivates assumptions on the data distribution. It is a graph with data points as vertices, where semantically similar data points are connected by sequences of edges. The bipartite graph (G’) consists of natural images (X) and augmented versions (tilde{X}) obtained by data augmentation. The collapsed graph (G) is obtained by collapsing (G’) onto the vertex set (X), where edges connect vertices with common neighbors in (G’). The augmentation graph offers insights into the relationships between neighboring images, which have small (ell_2) distance, and their common augmentations.
Formalizing the Regularizer
The augmentation graph allows for the formalization of the input consistency regularizer. The regularizer encourages the classifier (F) to predict the same class on all examples in a neighborhood (N(x)). The neighborhood (N(x)) consists of all examples connected to (x) by an edge in the augmentation graph. The self-training objective to be analyzed is the sum of the regularizer and the loss in fitting the pseudo-label, which is similar to empirically successful objectives.
Assumptions on the Data
To understand the usefulness of the regularizer, two key assumptions are made. In the idealized case of perfect input consistency ((R(F, x) = 0) for all (x)), enforcing perfect input consistency can be advantageous if the data satisfies a specific structure. For example, if the dog class is connected in the augmentation graph, enforcing perfect input consistency ensures that the classifier makes the same prediction on all dogs.
In conclusion, this blog post introduces the concept of self-training and its importance in leveraging unlabeled data. The necessity of regularization for self-training is highlighted, and the use of input consistency regularization is explained. The augmentation graph is introduced to formalize the regularizer and motivate assumptions on the data. Stay tuned for the next blog post, which will explore self-supervised contrastive learning algorithms.
Summary: Unlocking the Power of Unlabeled Data: A Comprehensive Guide to Deep Learning Algorithms – Self-training Demystified (Part 1)
Deep models often require a large number of training examples, but obtaining labeled data can be difficult and expensive. This has led to research on leveraging unlabeled data, such as crawling the web for unlabeled images. Recent studies have shown that models trained with unlabeled data can achieve performance comparable to fully-supervised models. This blog post series will discuss theoretical work analyzing the use of unlabeled data in machine learning algorithms. The first post focuses on self-training, an algorithmic paradigm for semi-supervised learning and domain adaptation. The importance of regularization in self-training is highlighted, and the concept of input consistency regularization is introduced. The augmentation graph is introduced as a key concept for formalizing the regularizer and making assumptions about the data distribution. The post ends by discussing the benefits of perfect input consistency and the properties of the data that enable it.
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