Semi-Supervised Learning is a machine learning paradigm that lies between supervised learning (where the model is trained on labeled data) and unsupervised learning (where the model deals with unlabeled data). In semi-supervised learning, the training dataset consists of a combination of labeled and unlabeled examples.

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Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation

Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation Semi-Supervised Domain Adaptation (SSDA) is a recently emerging research topic that extends from the widely-investigated Unsupervised Domain Adaptation (UDA) by further having a few target samples labeled, i.e., the model is trained with labeled source samples, unlabeled target samples as well as a few labeled target samples. Compared with UDA, the key to SSDA lies how to most effectively utilize the few labeled target samples. Existing SSDA approaches simply merge the few precious labeled target samples into vast labeled source samples or further align them, which dilutes the value of labeled target samples and thus still obtains a biased model. To remedy this, in this paper, we propose to decouple SSDA as an UDA problem and a semi-supervised learning problem where we first learn an UDA model using labeled source and unlabeled target samples and then adapt the learned UDA model in a semi-supervised way using labeled and unlabeled target samples. By utilizing the labeled source samples and target samples separately, the bias problem can be well mitigated. We further propose a consistency learning based mean teacher model to effectively adapt the learned UDA model using labeled and unlabeled target samples. Experiments show our approach outperforms existing methods.

DyCo: Dynamic, Contextualized AI Models

DyCo: Dynamic, Contextualized AI Models Devices with limited computing resources use smaller AI models to achieve low-latency inferencing. However, model accuracy is typically much lower than the accuracy of a bigger model that is trained and deployed in places where the computing resources are relatively abundant. We describe DyCo, a novel system that ensures privacy of stream data and dynamically improves the accuracy of small models used in devices. Unlike knowledge distillation or federated learning, DyCo treats AI models as black boxes. DyCo uses a semi-supervised approach to leverage existing training frameworks and network model architectures to periodically train contextualized, smaller models for resource-constrained devices. DyCo uses a bigger, highly accurate model in the edge-cloud to auto-label data received from each sensor stream. Training in the edge-cloud (as opposed to the public cloud) ensures data privacy, and bespoke models for thousands of live data streams can be designed in parallel by using multiple edge-clouds. DyCo uses the auto-labeled data to periodically re-train, stream-specific, bespoke small models. To reduce the periodic training costs, DyCo uses different policies that are based on stride, accuracy, and confidence information.We evaluate our system, and the contextualized models, by using two object detection models for vehicles and people, and two datasets (a public benchmark and another real-world proprietary dataset). Our results show that DyCo increases the mAP accuracy measure of small models by an average of 16.3% (and up to 20%) for the public benchmark and an average of 19.0% (and up to 64.9%) for the real-world dataset. DyCo also decreases the training costs for contextualized models by more than an order of magnitude.

T2-Net: A Semi-supervised Deep Model for Turbulence Forecasting

T2-Net: A Semi-supervised Deep Model for Turbulence Forecasting Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs. Traditional turbulence forecasting approaches heavily rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. The recent availability of high-resolution weather data and turbulence records allows more accurate forecasting of the turbulence in a data-driven way. However, it is a non-trivial task for developing a machine learning based turbulence forecasting system due to two challenges: (1) Complex spatio-temporal correlations, turbulence is caused by air movement with complex spatio-temporal patterns, (2) Label scarcity, very limited turbulence labels can be obtained. To this end, in this paper, we develop a unified semi-supervised framework, T2-Net, to address the above challenges. Specifically, we first build an encoder-decoder paradigm based on the convolutional LSTM to model the spatio-temporal correlations. Then, to tackle the label scarcity problem, we propose a novel Dual Label Guessing method to take advantage of massive unlabeled turbulence data. It integrates complementary signals from the main Turbulence Forecasting task and the auxiliary Turbulence Detection task to generate pseudo-labels, which are dynamically utilized as additional training data. Finally, extensive experimental results on a real-world turbulence dataset validate the superiority of our method on turbulence forecasting.

Active Adversarial Domain Adaptation

We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains. The former uses a domain discriminative model to align domains, while the latter utilizes the model to weigh samples to account for distribution shifts. Specifically, our importance weight promotes unlabeled samples with large uncertainty in classification and diversity compared to la-beled examples, thus serving as a sample selection scheme for active learning. We show that these two views can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not. AADA provides significant improvements over fine-tuning based approaches and other sampling methods when the two domains are closely related. Results on challenging domain adaptation tasks such as object detection demonstrate that the advantage over baseline approaches is retained even after hundreds of examples being actively annotated.

Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs

Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs Node classification in graph-structured data aims to classify the nodes where labels are only available for a subset of nodes. This problem has attracted considerable research efforts in recent years. In real-world applications, both graph topology and node attributes evolve over time. Existing techniques, however, mainly focus on static graphs and lack the capability to simultaneously learn both temporal and spatial/structural features. Node classification in temporal attributed graphs is challenging for two major aspects. First, effectively modeling the spatio-temporal contextual information is hard. Second, as temporal and spatial dimensions are entangled, to learn the feature representation of one target node, it’s desirable and challenging to differentiate the relative importance of different factors, such as different neighbors and time periods. In this paper, we propose STAR, a spatio-temporal attentive recurrent network model, to deal with the above challenges. STAR extracts the vector representation of neighborhood by sampling and aggregating local neighbor nodes. It further feeds both the neighborhood representation and node attributes into a gated recurrent unit network to jointly learn the spatio-temporal contextual information. On top of that, we take advantage of the dual attention mechanism to perform a thorough analysis on the model interpretability. Extensive experiments on real datasets demonstrate the effectiveness of the STAR model.

Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild

Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels. We propose that advantages may be derived by combining them, in the form of different insights that lead to a novel design and complementary properties that result in better performance. At the feature level, inspired by insights from semi-supervised learning, we propose a classification-aware domain adversarial neural network that brings target examples into more classifiable regions of source domain. Next, we posit that computer vision insights are more amenable to injection at the pixel level. In particular, we use 3D geometry and image synthesis based on a generalized appearance flow to preserve identity across pose transformations, while using an attribute-conditioned CycleGAN to translate a single source into multiple target images that differ in lower-level properties such as lighting. Besides standard UDA benchmark, we validate on a novel and apt problem of car recognition in unlabeled surveillance images using labeled images from the web, handling explicitly specified, nameable factors of variation through pixel-level and implicit, unspecified factors through feature-level adaptation.

Joint Pixel and Feature-level Domain Adaptation in the Wild

Joint Pixel and Feature-level Domain Adaptation in the Wild Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels. We propose that advantages may be derived by combining them, in the form of different insights that lead to a novel design and complementary properties that result in better performance. At the feature level, inspired by insights from semi-supervised learning in a domain adversarial neural network, we propose a novel regularization in the form of domain adversarial entropy minimization. Next, we posit that insights from computer vision are more amenable to injection at the pixel level and specifically address the key challenge of adaptation across different semantic levels. In particular, we use 3D geometry and image synthetization based on a generalized appearance flow to preserve identity across higher-level pose transformations, while using an attribute-conditioned CycleGAN to translate a single source into multiple target images that differ in lower-level properties such as lighting. We validate on a novel problem of car recognition in unlabeled surveillance images using labeled images from the web, handling explicitly specified, nameable factors of variation through pixel-level and implicit, unspecified factors through feature-level adaptation. Extensive experiments achieve state-of-the-art results, demonstrating the effectiveness of complementing feature and pixel-level information via our proposed domain adaptation method.