Transfer Learning for Cross-Dataset Recognition: A Survey <s> Unlabelled Source Dataset <s> We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabeled data follows the same class labels or generative distribution as the labeled data. Thus, we would like to use a large number of unlabeled images (or audio samples, or text documents) randomly downloaded from the Internet to improve performance on a given image (or audio, or text) classification task. Such unlabeled data is significantly easier to obtain than in typical semi-supervised or transfer learning settings, making self-taught learning widely applicable to many practical learning problems. We describe an approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data. These features form a succinct input representation and significantly improve classification performance. When using an SVM for classification, we further show how a Fisher kernel can be learned for this representation. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Unlabelled Source Dataset <s> Previous work on action recognition has focused on adapting hand-designed local features, such as SIFT or HOG, from static images to the video domain. In this paper, we propose using unsupervised feature learning as a way to learn features directly from video data. More specifically, we present an extension of the Independent Subspace Analysis algorithm to learn invariant spatio-temporal features from unlabeled video data. We discovered that, despite its simplicity, this method performs surprisingly well when combined with deep learning techniques such as stacking and convolution to learn hierarchical representations. By replacing hand-designed features with our learned features, we achieve classification results superior to all previous published results on the Hollywood2, UCF, KTH and YouTube action recognition datasets. On the challenging Hollywood2 and YouTube action datasets we obtain 53.3% and 75.8% respectively, which are approximately 5% better than the current best published results. Further benefits of this method, such as the ease of training and the efficiency of training and prediction, will also be discussed. You can download our code and learned spatio-temporal features here: http://ai.stanford.edu/∼wzou/ <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Unlabelled Source Dataset <s> Most modern research of facial beauty prediction focuses on geometric features by traditional machine learning methods. Geometric features may easily lose much feature information characterizing facial beauty, rely heavily on accurate manual landmark localization of facial features and impose strict restrictions on training samples. Deep architectures have been recently demonstrated to be a promising area of research in statistical machine learning. In this paper, deep self-taught learning is utilized to obtain hierarchical representations, learn the concept of facial beauty and produce human-like predictor. Deep learning is helpful to recognize a broad range of visual concept effectively characterizing facial beauty. Through deep learning, reasonable apparent features of face images are extracted without depending completely on artificial feature selection. Self-taught learning, which has the ability of automatically improving network systems to understand the characteristics of data distribution and making recognition significantly easier and cheaper, is used to relax strict restrictions of training samples. Moreover, in order to choose a more appropriate method for mapping high-level representations into beauty ratings efficiently, we compare the performance of five regression methods and prove that support vector machine (SVM) regression is better. In addition, novel applications of deep self-taught learning on local binary pattern (LBP) and Gabor filters are presented, and the improvements on facial beauty prediction are shown by deep self-taught learning combined with LBP. Finally, human-like performance is obtained with learning features in full-sized and high-resolution images. <s> BIB003 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Unlabelled Source Dataset <s> We consider a transfer-learning problem by using the parameter transfer approach, where a suitable parameter of feature mapping is learned through one task and applied to another objective task. Then, we introduce the notion of the local stability of parametric feature mapping and parameter transfer learnability, and thereby derive a learning bound for parameter transfer algorithms. As an application of parameter transfer learning, we discuss the performance of sparse coding in self-taught learning. Although self-taught learning algorithms with plentiful unlabeled data often show excellent empirical performance, their theoretical analysis has not been studied. In this paper, we also provide the first theoretical learning bound for self-taught learning. <s> BIB004 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Unlabelled Source Dataset <s> Visual representation is crucial for a visual tracking method's performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with target's motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to peform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favourably against several state-of-the-art trackers. <s> BIB005
This problem is similar to self-taught learning, where the source data are unlabelled but contain information (i.e. basic visual patterns) that can be used for target tasks. Feature Representation-based Transfer. BIB001 firstly presented the idea of "self-taught learning". They propose to construct higher-level features using sparse coding with the unlabelled source data. Recently, BIB004 provides a theoretical learning bound for self-taught learning with focus on discussing the performance of sparse coding in self-taught learning. The idea of self-taught learning has also been used in deep learning framework, where the unlabelled data are used for pretraining the network to obtain good starting point of parameters BIB002 BIB003 BIB005 . For instance, BIB002 propose a self-taught learning framework based on a deep Independent Subspace Analysis (ISA) network. They train the network on video blocks from UCF and Youtube datasets and use the learned model to extract features and recognize actions on Hollywood2 video clips. BIB003 use the unlabelled samples to pretrain the first layer of Convolutional deep belief network (CDBN) for initializing the network parameters. BIB005 use stacked convolutional autoencoders to learn the invariant representations from previous unlabelled image patches for visual tracking.