An Overview of Proxy-label Approaches for Semi-supervised Learning An Overview of Proxy-label Approaches for Semi-supervised Learning
Note: Parts of this post are based on my ACL 2018 paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift with Barbara Plank.... An Overview of Proxy-label Approaches for Semi-supervised Learning

Note: Parts of this post are based on my ACL 2018 paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift with Barbara Plank.

Table of contents:

  • Self-training
  • Multi-view training
    • Co-training
    • Democratic Co-learning
    • Tri-training
    • Tri-training with disagreement
    • Asymmetric tri-training
    • Multi-task tri-training
  • Self-ensembling
    • Ladder networks
    • Virtual Adversarial Training
    • ΠΠ model
    • Temporal Ensembling
    • Mean Teacher
  • Related methods and areas
    • Distillation
    • Learning from weak supervision
    • Learning with noisy labels
    • Data augmentation
    • Ensembling a single model

Unsupervised learning constitutes one of the main challenges for current machine learning models and one of the key elements that is missing for general artificial intelligence. While unsupervised learning on its own is still elusive, researchers have a made a lot of progress in combining unsupervised learning with supervised learning. This branch of machine learning research is called semi-supervised learning.

Semi-supervised learning has a long history. For a (slightly outdated) overview, refer to Zhu (2005) [1] and Chapelle et al. (2006) [2]. Particularly recently, semi-supervised learning has seen some success, considerably reducing the error rate on important benchmarks. Semi-supervised learning also makes an appearance in Amazon’s annual letter to shareholderswhere it is credited with reducing the amount of labelled data needed to achieve the same accuracy improvement by 40×40×.

In this blog post, I will focus on a particular class of semi-supervised learning algorithms that produce proxy labels on unlabelled data, which are used as targets together with the labelled data. These proxy labels are produced by the model itself or variants of it without any additional supervision; they thus do not reflect the ground truth but might still provide some signal for learning. In a sense, these labels can be considered noisy or weak. I will highlight the connection to learning from noisy labels, weak supervision as well as other related topics in the end of this post.

This class of models is of particular interest in my opinion, as a) deep neural networks have been shown to be good at dealing with noisy labels and b) these models have achieved state-of-the-art in semi-supervised learning for computer vision. Note that many of these ideas are not new and many related methods have been developed in the past. In one half of this post, I will thus cover classic methods and discuss their relevance for current approaches; in the other half, I will discuss techniques that have recently achieved state-of-the-art performance. Some of the following approaches have been referred to as self-teaching or bootstrapping algorithms; I am not aware of a term that captures all of them, so I will simply refer to them as proxy-labelmethods.

I will divide these methods in three groups, which I will discuss in the following: 1) self-training, which uses a model’s own predictions as proxy labels; 2) multi-view learning, which uses the predictions of models trained with different views of the data; and 3) self-ensembling, which ensembles variations of a model’s own predictions and uses these as feedback for learning. I will show pseudo-code for the most important algorithms. You can find the LaTeX source here.

There are many interesting and equally important directions for semi-supervised learning that I will not cover in this post, e.g. graph-convolutional neural networks [3].


Self-training (Yarowsky, 1995; McClosky et al., 2006) [45] is one of the earliest and simplest approaches to semi-supervised learning and the most straightforward example of how a model’s own predictions can be incorporated into training. As the name implies, self-training leverages a model’s own predictions on unlabelled data in order to obtain additional information that can be used during training. Typically the most confident predictions are taken at face value, as detailed next.

Formally, self-training trains a model mm on a labeled training set LL and an unlabeled data set UU. At each iteration, the model provides predictions m(x)m(x) in the form of a probability distribution over the CC classes for all unlabeled examples xx in UU. If the probability assigned to the most likely class is higher than a predetermined threshold ττxx is added to the labeled examples with p(x)=argmaxm(x)p(x)=argmax⁡m(x) as pseudo-label. This process is generally repeated for a fixed number of iterations or until no more predictions on unlabelled examples are confident. This instantiation is the most widely used and shown in Algorithm 1.
Classic self-training has shown mixed success. In parsing it proved successful with small datasets (Reichart, and Rappoport, 2007; Huang and Harper, 2009) [613] or when a generative component is used together with a reranker when more data is available (McClosky et al., 2006; Suzuki and Isozaki , 2008) [7]. Some success was achieved with careful task-specific data selection (Petrov and McDonald, 2012) [8], while others report limited success on a variety of NLP tasks (He and Zhou, 2011; Plank, 2011; Van Asch and Daelemans, 2016; van der Goot et al., 2017) [9101112].

The main downside of self-training is that the model is unable to correct its own mistakes. If the model’s predictions on unlabelled data are confident but wrong, the erroneous data is nevertheless incorporated into training and the model’s errors are amplified. This effect is exacerbated if the domain of the unlabelled data is different from that of the labelled data; in this case, the model’s confidence will be a poor predictor of its performance.

Multi-view training

Multi-view training aims to train different models with different views of the data. Ideally, these views complement each other and the models can collaborate in improving each other’s performance. These views can differ in different ways such as in the features they use, in the architectures of the models, or in the data on which the models are trained.

Co-training   Co-training (Blum and Mitchell, 1998) [17] is a classic multi-view training method, which makes comparatively strong assumptions. It requires that the data LL can be represented using two conditionally independent feature sets L1L1 and L2L2 and that each feature set is sufficient to train a good model. After the initial models m1m1 and m2m2 are trained on their respective feature sets, at each iteration, only inputs that are confident (i.e. have a probability higher than a threshold ττ) according to exactly one of the two models are moved to the training set of the other model. One model thus provides the labels to the inputs on which the othermodel is uncertain. Co-training can be seen in Algorithm 2. In the original co-training paper (Blum and Mitchell, 1998), co-training is used to classify web pages using the text on the page as one view and the anchor text of hyperlinks on other pages pointing to the page as the other view. As two conditionally independent views are not always available, Chen et al. (2011) [19] propose pseudo-multiview regularization (Chen et al., 2011) in order to split the features into two mutually exclusive views so that co-training is effective. To this end, pseudo-multiview regularization constrains the models so that at least one of them has a zero weight for each feature. This is similar to the orthogonality constraint recently used in domain adaptation to encourage shared and private spaces (Bousmalis et al., 2016) [20]. A second constraint requires the models to be confident on different subsets of UU. Chen et al. (2011) [18] use pseudo-multiview regularization to adapt co-training to domain adaptation.

Democratic Co-learning Rather than treating different feature sets as views, democratic co-learning (Zhou and Goldman, 2004) [21] employs models with different inductive biases. These can be different network architectures in the case of neural networks or completely different learning algorithms. Democratic co-learning first trains each model separately on the complete labelled data LL. The models then make predictions on the unlabelled data UU. If a majority of models confidently agree on the label of an example, the example is added to the labelled dataset. Confidence is measured in the original formulation by measuring if the sum of the mean confidence intervals ww of the models, which agreed on the label is larger than the sum of the models that disagreed. This process is repeated until no more examples are added. The final prediction is made with a majority vote weighted with the confidence intervals of the models. The full algorithm can be seen below. MM is the set of all models that predict the same label jj for an example xx.Tri-training   Tri-training (Zhou and Li, 2005) [14] is one of the best known multi-view training methods. It can be seen as an instantiation of democratic co-learning, which leverages the agreement of three independently trained models to reduce the bias of predictions on unlabeled data. The main requirement for tri-training is that the initial models are diverse. This can be achieved using different model architectures as in democratic co-learning. The most common way to obtain diversity for tri-training, however, is to obtain different variations SiSi of the original training data LL using bootstrap sampling. The three models m1m1m2m2, and m3m3 are then trained on these bootstrap samples, as depicted in Algorithm 4. An unlabeled data point is added to the training set of a model mimi if the other two models mjmj and mkmk agree on its label. Training stops when the classifiers do not change anymore. Despite having been proposed more than 10 years ago, before the advent of Deep Learning, we found in a recent paper (Ruder and Plank, 2018) [22] that classic tri-training is a strong baseline for neural semi-supervised with and without domain shift for NLP and that it outperforms even recent state-of-the-art methods.

Tri-training with disagreement   Tri-training with disagreement (Søgaard, 2010) [15] is based on the intuition that a model should only be strengthened in its weak points and that the labeled data should not be skewed by easy data points. In order to achieve this, it adds a simple modification to the original algorithm (altering line 8 in Algorithm 2), requiring that for an unlabeled data point on which mjmj and mkmk agree, the other model mimi disagrees on the prediction. Tri-training with disagreement is more data-efficient than tri-training and has achieved competitive results on part-of-speech tagging (Søgaard, 2010).

Asymmetric tri-training   Asymmetic tri-training (Saito et al., 2017) [16] is a recently proposed extension of tri-training that achieved state-of-the-art results for unsupervised domain adaptation in computer vision. For unsupervised domain adaptation, the test data and unlabeled data are from a different domain than the labelled examples. To adapt tri-training to this shift, asymmetric tri-training learns one of the models only on proxy labels and not on labelled examples (a change to line 10 in Algorithm 4) and uses only this model to classify target domain examples at test time. In addition, all three models share the same feature extractor.

Multi-task tri-training   Tri-training typically relies on training separate models on bootstrap samples of a potentially large amount of training data, which is expensive. Multi-task tri-training (MT-Tri) (Ruder and Plank, 2018) aims to reduce both the time and space complexity of tri-training by leveraging insights from multi-task learning (MTL) (Caruana, 1993) [23] to share knowledge across models and accelerate training. Rather than storing and training each model separately, MT-Tri shares the parameters of the models and trains them jointly using MTL. Note that the model does only pseudo MTL as all three models effectively perform the same task.

The output softmax layers are model-specific and are only updated for the input of the respective model. As the models leverage a joint representation, diversity is even more crucial. We need to ensure that the features used for prediction in the softmax layers of the different models are as diverse as possible, so that the models can still learn from each other’s predictions. In contrast, if the parameters in all output softmax layers were the same, the method would degenerate to self-training. Similar to pseudo-view regularization, we thus use an orthogonality constraint (Bousmalis et al., 2016) on two of the three softmax output layers as an additional loss term.

The pseudo-code can be seen below. In contrast to classic tri-training, we can train the multi-task model with its three model-specific outputs jointly and without bootstrap sampling on the labeled source domain data until convergence, as the orthogonality constraint enforces different representations between models m1m1 and m2m2. From this point, we can leverage the pair-wise agreement of two output layers to add pseudo-labeled examples as training data to the third model. We train the third output layer m3m3 only on pseudo-labeled target instances in order to make tri-training more robust to a domain shift. For the final prediction, we use majority voting of all three output layers. For more information about multi-task tri-training, self-training, other tri-training variants, you can refer to our recent ACL 2018 paper.


Self-ensembling methods are very similar to multi-view learning approaches in that they combine different variants of a model. Multi-task tri-training, for instance, can also be seen as a self-ensembling method where different variations of a model are used to create a stronger ensemble prediction. In contrast to multi-view learning, diversity is not a key concern. Self-ensembling approaches mostly use a single model under different configurations in order to make the model’s predictions more robust. Most of the following methods are very recent and several have achieved state-of-the-art results in computer vision.

Ladder networks   The ΓΓ (gamma) version of Ladder Networks (Rasmus et al., 2015) [24] aims to make a model more robust to noise. For each unlabelled example, it uses the model’s prediction on the clean example as a proxy label for prediction on a perturbed version of the example. This way, the model learns to develop features that are invariant to noise and predictive of the labels on the labelled training data. Ladder networks have been mostly used in computer vision where many forms of perturbation and data augmentation are available.

Virtual Adversarial Training   If perturbing the original sample is not possible or desired, we can instead perturb the example in feature space. Rather than randomly perturbing it by e.g. adding dropout, we can apply the worst possible perturbation for the model, which transforms the input into an adversarial sample. While adversarial training requires access to the labels to perform these perturbations, virtual adversarial training (Miyato et al., 2017) [25] requires no labels and is thus suitable for semi-supervised learning. Virtual adversarial training effectively seeks to make the model robust to perturbations in directions to which it is most sensitive and has achieved good results on text classification datasets.

ΠΠ model   Rather than treating clean predictions as proxy labels, the ΠΠ (pi) model (Laine and Aila, 2017) [27] ensembles the predictions of the model under two different perturbations of the input data and two different dropout conditions zz and z̃ z~. The full pseudo-code can be seen in Algorithm 6 below. g(x)g(x) is the stochastic input augmentation function. The first loss term encourages the predictions under the two different noise settings to be consistent, with λλdetermining the contribution, while the second loss term is the standard cross-entropy loss HHwith respect to the label yy. In contrast to the models we encountered before, we apply the unsupervised loss component to both unlabelled and labelled examples. Temporal Ensembling   Instead of ensembling over the same model under different noise configurations, we can ensemble over different models. As training separate models is expensive, we can instead ensemble the predictions of a model at different timesteps. We can save the ensembled proxy labels ZZ as an exponential moving average of the model’s past predictions on all examples as depicted below in order to save space. As we initialize the proxy labels as a zero vector, they are biased towards 00. We can correct this bias similar to Adam (Kingma and Ba, 2015) [28] based on the current epoch tt to obtain bias-corrected target vectors z̃ z~. We then update the model similar to the ΠΠ model.

Mean Teacher   Finally, instead of averaging the predictions of our model over training time, we can average the model weights. Mean teacher (Tarvainen and Valpola, 2017) [29] stores an exponential moving average of the model parameters. For every example, this mean teacher model is then used to obtain proxy labels z̃ z~. The consistency loss and supervised loss are computed as in temporal ensembling.

Mean teacher has achieved state-of-the-art results for semi-supervised learning for computer vision. For reference, on ImageNet with 10% of the labels, it achieves an error rate of 9.119.11, compared to an error rate of 3.793.79 using all labels with the state-of-the-art. For more information about self-ensembling methods, have a look at this intuitive blog post by the Curious AI company. We have run experiments with temporal ensembling for NLP tasks, but did not manage to obtain consistent results. My assumption is that the unsupervised consistency loss is more suitable for continuous inputs. Mean teacher might work better, as averaging weights aka Polyak averaging (Polyak and Juditsky, 1992) [33] is a tried method for accelerating optimization.

Very recently, Oliver et al. (2018) [39] raise some questions regarding the true applicability of these methods: They find that the performance difference to a properly tuned supervised baseline is smaller than typically reported, that transfer learning from a labelled dataset (e.g. ImageNet) outperforms the presented methods, and that performance degrades severely under a domain shift. In order to deal with the latter, algorithms such as asymmetric or multi-task tri-training learn different representations for the target distribution. It remains to be seen if these insights translate to other domains; a combination of transfer learning and semi-supervised adaptation to the target domain seems particularly promising.

Related methods and areas

Distillation   Proxy-label approaches can be seen as different forms of distillation (Hinton et al., 2015) [30]. Distillation was originally conceived as a method to compress the information of a large model or an ensemble in a smaller model. In the standard setup, a typically large and fully trained teacher model provides proxy targets for a student model, which is generally smaller and faster. Self-learning is akin to distillation without a teacher, where the student is left to learn by themselves and with no-one to correct its mistakes. For multi-view learning, different models work together to teach each other, alternately acting as both teachers and students. Self-ensembling, finally, has one model assuming the dual role of teacher and student: As a teacher, it generates new targets, which are then incorporated by itself as a student for learning.

Learning from weak supervision   Learning from weak supervision, as the name implies, can be seen as a weaker form of supervised learning or alternatively as a stronger form of semi-supervised learning: While supervised learning provides us with labels that we know to be correct and semi-supervised learning only provides us with a small set of labelled examples, weak supervision allows us to obtain labels that we know to be noisy for the unlabelled data as a further signal for learning. Typically, the weak annotator is an unsupervised method that is very different from the model we use for learning the task. For sentiment analysis, this could be a simple lexicon-based method [35]. Many of the presented methods could be extended to the weak supervision setting by incorporating the weak labels as feedback. Self-ensembling methods, for instance, might employ another teacher model that gauges the quality of weakly annotated examples similar to Deghani et al. (2018) [34].

Learning with noisy labels   Learning with noisy labels is similar to learning from weak supervision. In both cases, labels are available that cannot be completely trusted. For learning with noisy labels, labels are typically assumed to be permuted with a fixed random permutation. While proxy-label approaches supply the noisy labels themselves, when learning with noisy labels, the labels are part of the data. Similar to learning from weak supervision, we can try to model the noise to assess the quality of the labels (Sukhbaatar et al., 2015) [37]. Similar to self-ensembling methods, we can enforce consistency between the model’s preditions and the proxy labels (Reed et al., 2015) [36].

Data augmentation   Several self-ensembling methods employ data augmentation to enforce consistency between model predictions under different noise settings. Data augmentation is mostly used in computer vision, but noise in the form of different dropout masks can also be applied to the model parameters as in the ΠΠ model and has also been used in LSTMs (Zolna et al., 2018) [28]. While regularization in the form of dropout, batch normalization, etc. can be used when labels are available in order to make predictions more robust, a consistency loss is required in the case without labels. For supervised learning, adversarial training can be employed to obtain adversarial examples and has been used successfully e.g. for part-of-speech tagging (Yasunaga et al., 2018) [26].

Ensembling a single model   The discussed self-ensembling methods all employ ensemble predictions not just to make predictions more robust, but as feedback to improve the model itself during training in a self-reinforcing loop. In the supervised setting, this feedback might not be necessary; ensembling a single model is still useful, however, to save time compared to training multiple models. Two methods that have been proposed to ensemble a model from a single training run are checkpoint ensembles and snapshot ensembles. Checkpoint ensembles (Sennrich et al., 2016) [31] ensemble the last nn checkpoints of a single training run and have been used to achieve state-of-the-art in machine translation. Snapshot ensembles (Huang et al., 2017) [32] ensemble models converged to different minima during a training run and have been used to achieve state-of-the-art in object recognition.


I hope this post was able to give you an insight into a part of the semi-supervised learning landscape that seems to be particularly useful to improve the performance of current models. While learning completely without labelled data is unrealistic at this point, semi-supervised learning enables us to augment our small labelled datasets with large amounts of available unlabelled data. Most of the discussed methods are promising in that they treat the model as a black box and can thus be used with any existing supervised learning model. As always, if you have any questions or noticed any mistakes, feel free to write a comment in the comments section below.


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Original Source

Sebastian Ruder

Sebastian Ruder

My main research interests are at the intersection of natural language processing, machine learning, and deep learning. In the context of my research, I'm applying methods of these fields to cross-lingual sentiment analysis across different domains as well as aspect- and entity-based sentiment analysis. I'm interested in finding new algorithms and data sources to improve sentiment analysis systems that can be applied to other domains and tasks and adapting sentiment analysis systems to other languages by leveraging existing monolingual / bilingual data as well as generating new data.