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Title: Learning to cluster in order to transfer across domains and tasks

Paper content:
This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster.
The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.
We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not (pairwise semantic similarity).
This similarity is category-agnostic and can be learned from data in the source domain using a similarity network.
We then present two novel approaches for performing transfer learning using this similarity function.
First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs.
Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network.
Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches.
Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet.
Our results show that we can reconstruct semantic clusters with high accuracy.
We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets.
Our approach doesn't explicitly deal with domain discrepancy.
If we combine with a domain adaptation loss, it shows further improvement.
Supervised learning has made significant strides in the past decade, with substantial advancements arising from the use of deep neural networks.
However, a large part of this success has come from the existence of extensive labeled datasets.
In many situations, it is not practical to obtain such data due to the amount of effort required or when the task or data distributions change dynamically.
To deal with these situations, the fields of transfer learning and domain adaptation have explored how to transfer learned knowledge across tasks or domains.
Many approaches have focused on cases where the distributions of the features and labels have changed, but the task is the same (e.g., classification across datasets with the same categories).
Cross-task transfer learning strategies, on the other hand, have been widely adopted especially in the computer vision community where features learned by a deep neural network on a large classification task have been applied to a wide variety of other tasks (Donahue et al., 2014) .Most
of the prior cross-task transfer learning works, however, require labeled target data to learn classifiers for the new task. If labels
of the target data are absent, there is little choice other than to apply unsupervised approaches such as clustering on the target data with pre-trained feature representations. In this paper
, we focus on the question of what can be transferred (besides features) to support both cross-domain and cross-task transfer learning. We address it
with a learned similarity function as the fundamental component of clustering. Clustering can
then be realized using a neural network trained using the output of the similarity function, which can be successfully used to achieve both cross-task and cross-domain transfer.The key idea is to formulate the clustering objective to use a learnable (and transferable) term, which in our proposed work is a similarity prediction function. Our proposed objective
function can be easily combined with deep neural networks and optimized end-to-end. The features and clustering
are optimized jointly, hence taking advantage of such side information in a robust way. Using this method, we show
that unsupervised learning can benefit from learning performed on a distinct task, and demonstrate the flexibility of further combining it with a classification loss and domain discrepancy loss.In summary, we make several contributions. First, we propose to use predictive
pairwise similarity as the knowledge that is transferred and formulate a learnable objective function to utilize the pairwise information in a fashion similar to constrained clustering. We then provide the methodologies to
deploy the objective function in both cross-task and cross-domain scenarios with deep neural networks. The experimental results for cross-task
learning on Omniglot and ImageNet show that we can achieve state of the art clustering results with predicted similarities. On the standard domain adaptation benchmark
Office-31 dataset, we demonstrate improvements over state-of-art even when not performing any explicit domain adaptation, and further improvements if we do. Finally, on another domain adaptation task,
SVHN-to-MNIST, our approach using Omniglot as the auxiliary dataset achieves top performance with a large margin.
We report the average performance over the 20 alphabets in table 1.
Our approach achieved the top performance on both metrics.
The CCN demonstrates strong robustness on the challenging scenario of unknown K. It achieved 78.1% average accuracy.
Compared with 82.4% when K is known, CCN has a relatively small drop.
Compared to the second best algorithm, CSP, which is 65.4%, CCN outperforms it with a large gap.
The classical approach MPCK-means works surprisingly well when the number of clusters is known, but its performance dropped dramatically from 81.9% to 53.9% when K = 100.
In the performance breakdown for the 20 individual alphabets, CCN achieved 94% clustering accuracy on Old Church Slavonic Cyrillic, which has 45 characters (appendix TAB4 ).
Therefore the results show the feasibility of reconstructing semantic clusters using only noisy similarity predictions.When to use the semantic similarity?
The experiments in table 1 show a clear trend that utilizing the pairwise constraints jointly for both metric learning and minimizing the clustering loss achieves the best performance, including both MPCK-means and CCN.
In the case of unknown number of clusters, where we set K = 100, the algorithms that use constraints to optimize clustering loss have better robustness, for example, CSP and CCN.
The group that only use constraints for metric learning (ITML, SKMS, SKKm, and SKLR) significantly outperform the group that does not use it (K-means, LPNMF, LSC).
However, their performance are still far behind CCN.
Our results confirm the importance of jointly optimizing the metric and clustering.The robustness against noisy similarity prediction is the key factor to enable the cross-task transfer framework.
To the best of our knowledge, table 1 is the first comprehensive robustness comparisons using predicted constraints learned from real data instead of converting from ground-truth labels.
The accuracy of G in our experiment is shown in appendix table 7 and demonstrates the reasonable performance of G which is on par with Matching-Net BID34 .
After binarizing the prediction at 0.5 probability, the similar pair precision, similar pair recall, dissimilar pair precision, and dissimilar pair recall among the 659 characters are (0.392, 0.927, 0.999, 0.995), accordingly.
The binarized predictions are better than uniform random guess (0.002, 0.500, 0.998, 0.500), but are still noisy.
Therefore it is very challenging for constrained clustering.
The visualization of the robustness range of CCN are provided in appendix D, and shows that the robustness is related to the density of pairs involved in a mini-batch.
We hypothesize that during the optimization, the gradients from wrongly predicted pairs are canceled out by each other or by the correctly predicted pairs.
Therefore the overall gradient still moves the solution towards a better clustering result.How to predict K?
Inferring the number of clusters (N C) is a hard problem, but with the pairwise similarity information, it becomes feasible.
For evaluation, we compute the difference between the number of dominant clusters (N DC) and the true number of categories (N C gt ) in a dataset.
We use a naive definition for N DC, which is the number of clusters that have a size larger than expected size when data is sampled uniformly.
In other words, TAB5 ).
We compare this with the baseline approach SKMS BID1 , which does not require a given K and supports a pipeline to estimate K automatically (therefore we only put it into the column K = 100 in table 1.).
SKMS gets 16.3.
Furthermore, 10 out of 20 datasets from CCN's prediction have a difference between N DC d and N C gt d smaller or equal to 3, which shows the feasibility of estimating K with predicted similarity.
DISPLAYFORM0
The results are summarized in table 2.
Our approach (CCN + ) demonstrates a strong performance boost for the unsupervised cross-domain transfer problem.
It reaches 77.5% average accuracy which gained 6.2 points from the 71.3% source-only baseline.
Although our approach merely transfers more information from the auxiliary dataset, it outperforms the strong approach DANN (75.7%), and state-of-the-art JAN (76.9%).
When combining ours with DANN (CCN ++ ), the performance is further boosted.
This indicates that LCO helps mitigate the transfer problem in a certain way that is orthogonal to minimizing the domain discrepancy.
We observe the same trend when using a deeper backbone network, i.e., ResNet-34.
In such a case the average accuracy achieved is 77.9%, 81.1% and 82% for source-only, CCN + and CCN ++ , respectively, though we used exactly the same G as before (with ResNet-18 backbone for G).
This indicates that the information carried in the similarity predictions is not equivalent to transferring features with deeper networks.
More discussions are in appendix C and the performance of G is provided in appendix table 11 to show that although the prediction has low precision for similar pairs (∼ 0.2), our approach still benefits from the dense similarity predictions.
In this paper, we demonstrated the usefulness of transferring information in the form of pairwise similarity predictions.
Such information can be transferred as a function and utilized by a loss formulation inspired from constrained clustering, but implemented more robustly within a neural network that can jointly optimize both features and clustering outputs based on these noisy predictions.
The experiments for both cross-task and cross-domain transfer learning show strong benefits of using the semantic similarity predictions resulting in new state of the art results across several datasets.
This is true even without explicit domain adaptation for the cross-domain task, and if we add a domain discrepancy loss the benefits increase further.There are two key factors that determine the performance of the proposed framework.
The first is the robustness of the constrained clustering and second is the performance of the similarity prediction function.
We show robustness of CCN empirically, but we do not explore situations where learning the similarity function is harder.
For example, such cases arise when there are a small number of categories in source or a large domain discrepancy between source and target.
One idea to deal with such a situation is learning G with domain adaptation strategies.
We leave these aspects for future work.
The resulting performance w.r.t different values of recall, density, and number of clusters is visualized in FIG7 .
A bright color means high NMI score and is desired.
The larger the bright region, the more robust the clustering is against the noise of similarity prediction.
The ACC score shows almost the same trend and is thus not shown here.How does similarity prediction affect clustering?
Looking at the top-left heat map in FIG7 , which has D = 1 and 10 clusters, it can be observed that the NMI score is very robust to low similar pair recall, even lower than 0.5.
For recall of dissimilar pairs, the effect of recall is divided at the 0.5 value: the clustering performance can be very robust to noise in dissimilar pairs if the recall is greater than 0.5; however, it can completely fail if recall is below 0.5.
For similar pairs, the clustering works on a wide range of recalls when the recall of dissimilar pairs is high.In practical terms, robustness to the recall of similar pairs is desirable because it is much easier to predict dissimilar pairs than similar pairs in real scenarios.
In a dataset with 10 categories e.g. Cifar-10, we can easily get 90% recall for dissimilar pairs with purely random guess if the number of classes is known, while the recall for similar pairs will be 10%.How
does the density of the constraints affect clustering? We
argue that the density of pairwise relationships is the key factor to improving the robustness of clustering. The
density D = 1 means that every pair in a mini-batch is utilized by the clustering loss. For
density D = 0.1, it means only 1 out of 10 possible constraints is used. We
could regard the higher density as better utilization of the pairwise information in a mini-batch, thus more learning instances contribute to the gradients at once. Consider
a scenario where there is one sample associated with 5 true similar pairs and 3 false similar pairs. In such
a case, the gradients introduced by the false similar pairs have a higher chance to be overridden by true similar pairs within the mini-batch, thus the loss can converge faster and is less affected by errors. In FIG7
, we could see when density decreases, the size of the bright region shrinks significantly.In our implementation, enumerating the full pairwise relationships introduces negligible overhead in computation time using GPU. Although
there is overhead for memory consumption, it is limited because only the vector of predicted distributions has to be enumerated for calculating the clustering loss.The effect of varying the number of Clusters In the MNIST experiments, the number of categories is 10. We augment
the softmax output number up to 100. The rows of
FIG7 show that even when the number of output categories is significant larger than the number of true object categories, e.g. 100 > 10, the clustering performance NMI score only degrades slightly.

assistant: text: A learnable clustering objective to facilitate transfer learning across domains and tasks