Unsupervised learning is a branch of machine learning that learns from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. (Wikipedia)
Since I had zero experience with generative adversarial networks, I thought I should document some problems I had to overcome. Quoting Wikipedia: "A generative adversarial network (GAN) is a class of machine learning systems. This technique can generate photographs that look at least superficially authentic to human observers, having many realistic characteristics. It is a form of unsupervised learning." I'm not doing any introduction about how a GAN works since there are a lot of materials online with far better insights than the ones I could give.
Most of the successful commercial applications to date--in areas such as computer vision, speech recognition, machine translation, and natural language processing--have involved supervised learning, taking advantage of labeled datasets. However, most of the world's data is unlabeled. In this book, we will cover the field of unsupervised learning (which is a branch of machine learning used to find hidden patterns) and learn the underlying structure in unlabeled data. According to many industry experts, such as Yann LeCun, the Director of AI Research at Facebook and a professor at NYU, unsupervised learning is the next frontier in AI and may hold the key to AGI. For this and many other reasons, unsupervised learning is one of the trendiest topics in AI today.
Is there a principled way to learn a probabilistic discriminative classifier from an unlabeled data set? We present a framework that simultaneously clusters the data and trains a discriminative classifier. We call it Regularized Information Maximization (RIM). The approach can flexibly incorporate different likelihood functions, express prior assumptions about the relative size of different classes and incorporate partial labels for semi-supervised learning. Our empirical evaluation indicates that RIM outperforms existing methods on several real data sets, and demonstrates that RIM is an effective model selection method.
When the distribution of unlabeled data in feature space lies along a manifold, the information it provides may be used by a learner to assist classification in a semi-supervised setting. While manifold learning is well-known in machine learning, the use of manifolds in human learning is largely unstudied. We perform a set of experiments which test a human's ability to use a manifold in a semi-supervised learning task, under varying conditions. We show that humans may be encouraged into using the manifold, overcoming the strong preference for a simple, axis-parallel linear boundary. Papers published at the Neural Information Processing Systems Conference.
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efficiently compute a value and gradients of the marginal likelihood for unlabeled objects. The conditional part of the model is based on a proposed conditional coupling layer. We demonstrate performance of the model for semi-supervised classification problem on different datasets. The model outperforms the baseline approach based on variational auto-encoders on MNIST dataset.
Sparse Subspace Clustering (SSC) is one of the most popular methods for clustering data points into their underlying subspaces. However, SSC may suffer from heavy computational burden. Orthogonal Matching Pursuit applied on SSC accelerates the computation but the trade-off is the loss of clustering accuracy. In this paper, we propose a noise-robust algorithm, Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering (RCOMP-SSC), to improve the clustering accuracy and maintain the low computational time by restricting the number of connections of each data point during the iteration of OMP. Also, we develop a framework of control matrix to realize RCOMP-SCC. And the framework is scalable for other data point selection strategies. Our analysis and experiments on synthetic data and two real-world databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared with other clustering methods in terms of accuracy and computational time.
We propose a simple yet effective method to learn to segment new indoor scenes from an RGB-D sequence: State-of-the-art methods trained on one dataset, even as large as SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision. To make semantic segmentation more useful in practice, we learn to segment new indoor scenes from sequences without manual annotations by exploiting geometric constraints and readily available training data from SUNRGB-D. As a result, we can then robustly segment new images of these scenes from color information only. To efficiently exploit geometric constraints for our purpose, we propose to cast these constraints as semi-supervised terms, which enforce the fact that the same class should be predicted for the projections of the same 3D location in different images. We show that this approach results in a simple yet very powerful method, which can annotate sequences of ScanNet and our own sequences using only annotations from SUNRGB-D.
Recent work has shown that using unlabeled data in semi-supervised learning is not always beneficial and can even hurt generalization, especially when there is a class mismatch between the unlabeled and labeled examples. We investigate this phenomenon for image classification on the CIFAR-10 and the ImageNet datasets, and with many other forms of domain shifts applied (e.g. salt-and-pepper noise). Our main contribution is Split Batch Normalization (Split-BN), a technique to improve SSL when the additional unlabeled data comes from a shifted distribution. We achieve it by using separate batch normalization statistics for unlabeled examples. Due to its simplicity, we recommend it as a standard practice. Finally, we analyse how domain shift affects the SSL training process. In particular, we find that during training the statistics of hidden activations in late layers become markedly different between the unlabeled and the labeled examples.
Deep neural networks are known to suffer from catastrophic forgetting in class-incremental learning, where the performance on previous tasks drastically degrades when learning a new task. To alleviate this effect, we propose to leverage a continuous and large stream of unlabeled data in the wild. In particular, to leverage such transient external data effectively, we design a novel class-incremental learning scheme with (a) a new distillation loss, termed global distillation, (b) a learning strategy to avoid overfitting to the most recent task, and (c) a sampling strategy for the desired external data. Our experimental results on various datasets, including CIFAR and ImageNet, demonstrate the superiority of the proposed methods over prior methods, particularly when a stream of unlabeled data is accessible: we achieve up to 9.3% of relative performance improvement compared to the state-of-the-art method.