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Title: Unsupervised Clustering using Pseudo-semi-supervised Learning

Paper content:
In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance.
To leverage semi-supervised models, we first need to automatically generate labels, called pseudo-labels.
We find that prior approaches for generating pseudo-labels hurt clustering performance because of their low accuracy.
Instead, we use an ensemble of deep networks  to construct a similarity graph, from which we extract high accuracy pseudo-labels.
The approach of finding high quality pseudo-labels using ensembles and training the semi-supervised model is iterated, yielding continued improvement.
We show that our approach outperforms state of the art clustering results for multiple image and text datasets.
For example, we achieve 54.6% accuracy for CIFAR-10 and 43.9% for 20news, outperforming state of the art by 8-12% in absolute terms.
Semi-supervised methods, which make use of large unlabelled data sets and a small labelled data set, have seen recent success, e.g., ladder networks Rasmus et al. (2015) achieves 99% accuracy in MNIST using only 100 labelled samples.
These approaches leverage the unlabelled data to help the network learn an underlying representation, while the labelled data guides the network towards separating the classes.
In this paper, we ask two questions: is it possible to create the small labelled data set required by semi-supervised methods purely using unsupervised techniques?
If so, can semi-supervised methods leverage this autonomously generated pseudo-labelled data set to deliver higher performance than state-of-the-art unsupervised approaches?
We answer both these questions in the affirmative.
We first find that prior approaches for identifying pseudo-labels Caron et al. (2018) ; Chen (2018); Lee (2013) perform poorly because of their low accuracy (Section 2).
To create a high accuracy pseudo-labelled data set autonomously, we use a combination of ensemble of deep networks with a custom graph clustering algorithm (Section 4).
We first train an ensemble of deep networks in an unsupervised manner.
Each network independently clusters the input.
We then compare two input data points.
If all of the networks agree that these two data points belong to the same cluster, we can be reasonably sure that these data points belong to the same class.
In this way, we identify all input data pairs belonging to the same class with high precision in a completely unsupervised manner.
In the next step, we use these high quality input pairs to generate a similarity graph, with the data points as nodes and edges between data points which are deemed to be similar by our ensemble.
From this graph, we extract tight clusters of data points, which serve as pseudo-labels.
Note that, in this step, we do not cluster the entire dataset, but only a small subset on which we can get high precision.
Extracting high quality clusters from this graph while ensuring that the extracted clusters correspond to different classes is challenging.
We discuss our approach in Section 4.2.1 for solving this problem.
In this way, our method extracts unambiguous samples belonging to each class, which serves as pseudo-labels for semi-supervised learning.
For semi-supervised learning using the labels generated above, one could use ladder networks Rasmus et al. (2015) .
However, we found that ladder networks is unsuitable for the initial unsupervised clustering step as it can degenerate to outputting constant values for all inputs in the absence of unsupervised loss.
To enable unsupervised clustering, we augment ladder networks using information maximization Krause et al. (2010) to create the Ladder-IM, and with a dot product loss to create Ladder-Dot.
We show in Section 5 that Ladder-IM and Ladder-Dot, by themselves, also provide improvements over previous state of the art.
We use the same models for both the first unsupervised learning step as well as the subsequent pseudo-semi-supervised iterations.
Finally, the approach of finding high quality clusters using an ensemble, and using them as labels to train a new ensemble of semi-supervised models, is iterated, yielding continued improvements.
The large gains of our method mainly come from this iterative approach, which can in some cases, yield upto 17% gains in accuracy over the base unsupervised models (see section 5.5).
We name our pseudo-semi-supervised learning approach Kingdra 1 .
Kingdra is independent of the type of data set; we show examples of its use on both image and text data sets in Section 5.
This is in contrast to some previous approaches using CNNs, e.g. Chang et al. (2017) , Caron et al. (2018) , which are specialized for image data sets.
We perform unsupervised classification using Kingdra on several standard image (MNIST, CIFAR10, STL) and text (reuters, 20news) datasets.
On all these datasets, Kingdra is able to achieve higher clustering accuracy compared to current state-of-the-art deep unsupervised clustering techniques.
For example, on the CIFAR10 and 20news datasets, Kingdra is able to achieve classification accuracy of 54.6% and 43.9%, respectively, delivering 8-12% absolute gains over state of the art results Hu et al. (2017) ; Xie et al. (2016) .
Several techniques have been proposed in the literature for generating pseudo-labels (Caron et al. (2018) ; Chen (2018); Lee (2013) .
In Lee (2013) , the output class with the highest softmax value (Argmax) is taken to be the pseudo-label.
In Caron et al. (2018) , the authors perform K-means clustering on the feature vector and use the K-means clusters as pseudo-labels.
Finally, authors in Chen (2018) treat the softmax output as confidence and only label those items whose confidence value is above a high threshold.
Note that none of these techniques for identifying pseudo-labels have been applied in our context, i.e., for unsupervised clustering using semi-supervised models.
In this paper, we introduced Kingdra, a novel pseudo-semi-supervised learning approach for clustering.
Kingdra outperforms current state-of-the-art unsupervised deep learning based approaches, with 8-12% gains in absolute accuracy for CIFAR10 and 20news datasets.
As part of Kingdra, we proposed clustering ladder networks, Ladder-IM and Ladder-Dot, that works well in both unsupervised and semi-supervised settings.
While Kingdra performs well in the datasets we studied, the similarity-based graph clustering algorithm used has difficulty as the number of classes increase.
For example, for the datasets we evaluated, the t pos and t neg can be simply set to the number of models in the ensemble.
However, as the number of classes increase, these thresholds may need some tuning.
For CIFAR100, with 100 classes, our graph clustering algorithm is not able to identify 100 diverse classes effectively.
We are looking at improving the clustering algorithm as part of future work.
We are also evaluating adding diversity to the models in the ensemble, either via changing the model structure, size and/or through changing the standard deviation of random noise used in ladder networks.

assistant: text: Using ensembles and pseudo labels for unsupervised clustering