241 research outputs found
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing
This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep RL for image processing are still limited. Therefore, we
extend deep RL to pixelRL for various image processing applications. In
pixelRL, each pixel has an agent, and the agent changes the pixel value by
taking an action. We also propose an effective learning method for pixelRL that
significantly improves the performance by considering not only the future
states of the own pixel but also those of the neighbor pixels. The proposed
method can be applied to some image processing tasks that require pixel-wise
manipulations, where deep RL has never been applied. We apply the proposed
method to three image processing tasks: image denoising, image restoration, and
local color enhancement. Our experimental results demonstrate that the proposed
method achieves comparable or better performance, compared with the
state-of-the-art methods based on supervised learning.Comment: Accepted to AAAI 201
BSED: Baseline Shapley-Based Explainable Detector
Explainable artificial intelligence (XAI) has witnessed significant advances
in the field of object recognition, with saliency maps being used to highlight
image features relevant to the predictions of learned models. Although these
advances have made AI-based technology more interpretable to humans, several
issues have come to light. Some approaches present explanations irrelevant to
predictions, and cannot guarantee the validity of XAI (axioms). In this study,
we propose the Baseline Shapley-based Explainable Detector (BSED), which
extends the Shapley value to object detection, thereby enhancing the validity
of interpretation. The Shapley value can attribute the prediction of a learned
model to a baseline feature while satisfying the explainability axioms. The
processing cost for the BSED is within the reasonable range, while the original
Shapley value is prohibitively computationally expensive. Furthermore, BSED is
a generalizable method that can be applied to various detectors in a
model-agnostic manner, and interpret various detection targets without
fine-grained parameter tuning. These strengths can enable the practical
applicability of XAI. We present quantitative and qualitative comparisons with
existing methods to demonstrate the superior performance of our method in terms
of explanation validity. Moreover, we present some applications, such as
correcting detection based on explanations from our method
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
This paper tackles unpaired image enhancement, a task of learning a mapping
function which transforms input images into enhanced images in the absence of
input-output image pairs. Our method is based on generative adversarial
networks (GANs), but instead of simply generating images with a neural network,
we enhance images utilizing image editing software such as Adobe Photoshop for
the following three benefits: enhanced images have no artifacts, the same
enhancement can be applied to larger images, and the enhancement is
interpretable. To incorporate image editing software into a GAN, we propose a
reinforcement learning framework where the generator works as the agent that
selects the software's parameters and is rewarded when it fools the
discriminator. Our framework can use high-quality non-differentiable filters
present in image editing software, which enables image enhancement with high
performance. We apply the proposed method to two unpaired image enhancement
tasks: photo enhancement and face beautification. Our experimental results
demonstrate that the proposed method achieves better performance, compared to
the performances of the state-of-the-art methods based on unpaired learning.Comment: Accepted to AAAI 202
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