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Pingbo Pan

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3 papers

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AAAI Conference 2020 Conference Paper

Adversarial Localized Energy Network for Structured Prediction

Pingbo Pan
Ping Liu
Yan Yan
Tianbao Yang
Yi Yang

This paper focuses on energy model based structured output prediction. Though inheriting the beneﬁts from energybased models to handle the sophisticated cases, previous deep energy-based methods suffered from the substantial computation cost introduced by the enormous amounts of gradient steps in the inference process. To boost the efﬁciency and accuracy of the energy-based models on structured output prediction, we propose a novel method analogous to the adversarial learning framework. Speciﬁcally, in our proposed framework, the generator consists of an inference network while the discriminator is comprised of an energy network. The two sub-modules, i. e. , the inference network and the energy network, can beneﬁt each other mutually during the whole computation process. On the one hand, our modiﬁed inference network can boost the efﬁciency by predicting good initializations and reducing the searching space for the inference process; On the other hand, inheriting the beneﬁts of the energy network, the energy module in our network can evaluate the quality of the generated output from the inference network and correspondingly provides a resourceful guide to the training of the inference network. In the ideal case, the adversarial learning strategy makes sure the two sub-modules can achieve an equilibrium state after steps. We conduct extensive experiments to verify the effectiveness and efﬁciency of our proposed method.

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NeurIPS Conference 2020 Conference Paper

Continual Deep Learning by Functional Regularisation of Memorable Past

Pingbo Pan
Siddharth Swaroop
Alexander Immer
Runa Eschenhagen
Richard Turner
Mohammad Emtiyaz Khan

Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past. Recent works address this with weight regularisation. Functional regularisation, although computationally expensive, is expected to perform better, but rarely does so in practice. In this paper, we fix this issue by using a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting. By using a Gaussian Process formulation of deep networks, our approach enables training in weight-space while identifying both the memorable past and a functional prior. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.

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AAAI Conference 2020 Conference Paper

EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network

Minfeng Zhu
Pingbo Pan
Wei Chen
Yi Yang

This work focuses on the extremely low-light image enhancement, which aims to improve image brightness and reveal hidden information in darken areas. Recently, image enhancement approaches have yielded impressive progress. However, existing methods still suffer from three main problems: (1) low-light images usually are high-contrast. Existing methods may fail to recover images details in extremely dark or bright areas; (2) current methods cannot precisely correct the color of low-light images; (3) when the object edges are unclear, the pixel-wise loss may treat pixels of different objects equally and produce blurry images. In this paper, we propose a two-stage method called Edge-Enhanced Multi-Exposure Fusion Network (EEMEFN) to enhance extremely low-light images. In the ﬁrst stage, we employ a multi-exposure fusion module to address the high contrast and color bias issues. We synthesize a set of images with different exposure time from a single image and construct an accurate normal-light image by combining well-exposed areas under different illumination conditions. Thus, it can produce realistic initial images with correct color from extremely noisy and low-light images. Secondly, we introduce an edge enhancement module to reﬁne the initial images with the help of the edge information. Therefore, our method can reconstruct high-quality images with sharp edges when minimizing the pixel-wise loss. Experiments on the See-in-the-Dark dataset indicate that our EEMEFN approach achieves state-of-the-art performance.

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