Real-time Bidding (RTB) and Guaranteed Display (GD) advertising are two primary ways to sell impressions for publishers in online display advertising. Although GD contract serves less efficiently compared to RTB ads, it helps advertisers reach numerous target audiences at a lower cost and allows publishers to increase overall advertising revenue. However, with billion-scale requests online per day, it’s a challenging problem for publishers to decide whether and which GD ad to display for each impression. In this paper, we propose an optimal allocation model for GD contracts considering optimizing three objectives: maximizing guaranteed delivery and impressions’ quality and minimizing the extra traffic cost of GD contracts to increase overall revenue. The traffic cost of GD contracts is defined as the potential expected revenue if the impression is allocated to RTB ads. Our model dynamically adjusts the weights for each GD contract between impressions’ quality and traffic cost based on real-time performance, which produces fairness-aware allocation results. A parallel training framework based on Parameter-Server (PS) architecture is utilized to efficiently and periodically update the model. Deriving from the allocation model, we also propose a simple and adaptive online bidding strategy for GD contracts, which can be updated quickly by feedback-based algorithms to achieve optimal impression allocation even in complex and dynamic environments. We demonstrate the effectiveness of our proposed method by using both offline evaluation and online A/B testing.

Image aesthetic quality assessment has gained the enormous interest in recent years with the advancement of deep learning and the large-scale datasets. Current state-of-the-art methods generally leverage deep features, while the hand-crafted photographic attributes which are also useful but not always available have not drawn the sufficient attention. In this paper, we propose the Photographic Embedding for Aesthetic Rating (PEAR) framework to assimilate their advantages. In PEAR, a prior network is constructed to smoothly inject the photographic attributes to the latent space, and handle the attribute missing case via knowledge transfer. Simultaneously, aesthetic quality assessment is formulated as multi-task learning of aesthetic rating and image reconstruction to regularize the learning of latent codes. The extensive experiments on two challenging datasets demonstrate PEAR outperforms the state-of-the-art methods.

Learning with noisy labels has gained the enormous interest in the robust deep learning area. Recent studies have empir-ically disclosed that utilizing dual networks can enhance the performance of single network but without theoretic proof. In this paper, we propose Cooperative Learning (CooL) frame-work for noisy supervision that analytically explains the ef-fects of leveraging dual or multiple networks. Specifically, the simple but efficient combination in CooL yields a more reliable risk minimization for unseen clean data. A range of experiments have been conducted on several benchmarks with both synthetic and real-world settings. Extensive results indi-cate that CooL outperforms several state-of-the-art methods.

Learning with noisy labels is imperative in the Big Data era since it reduces expensive labor on accurate annotations. Previous method, learning with noise transition, has enjoyed theoretical guarantees when it is applied to the scenario with the class-conditional noise. However, this approach critically depends on an accurate pre-estimated noise transition, which is usually impractical. Subsequent improvement adapts the preestimation in the form of a Softmax layer along with the training progress. However, the parameters in the Softmax layer are highly tweaked for the fragile performance and easily get stuck into undesired local minimums. To overcome this issue, we propose a Latent Class-Conditional Noise model (LCCN) that models the noise transition in a Bayesian form. By projecting the noise transition into a Dirichlet-distributed space, the learning is constrained on a simplex instead of some adhoc parametric space. Furthermore, we specially deduce a dynamic label regression method for LCCN to iteratively infer the latent true labels and jointly train the classifier and model the noise. Our approach theoretically safeguards the bounded update of the noise transition, which avoids arbitrarily tuning via a batch of samples. Extensive experiments have been conducted on controllable noise data with CIFAR10 and CIFAR-100 datasets, and the agnostic noise data with Clothing1M and WebVision17 datasets. Experimental results have demonstrated that the proposed model outperforms several state-of-the-art methods.

Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels. We examine this behavior in light of the Shannon entropy of the predictions and demonstrate the low entropy predictions determined by a given threshold are much more reliable as the supervision than the original noisy labels. It also shows the advantage in maintaining more training samples than previous methods. Then, we power this entropy criterion with the Collaborative Label Correction (CLC) framework to further avoid undesired local minimums of the single network. A range of experiments have been conducted on multiple benchmarks with both synthetic and real-world settings. Extensive results indicate that our CLC outperforms several state-of-the-art methods.