Data Mining Practical Machine Learning Tools and Techniques

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge[26].

/pdf/enhanced-deep-residual-networks-for-single-image-super-12kr7yvkfk.pdf

Enhanced Deep Residual Networks for Single Image Super-Resolution

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Ex-ploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.

/pdf/unsupervised-monocular-depth-estimation-with-left-right-1s6azbk2bd.pdf

Unsupervised Monocular Depth Estimation with Left-Right Consistency

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters A key challenge is to handle the increased amount of data and extended training time We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in single-machine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al, 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al, 2013a)) Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is $\ell _2$ . In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.

Loss Functions for Image Restoration With Neural Networks

Structural pruning of neural network parameters reduces computational, energy, and memory transfer costs during inference. We propose a novel method that estimates the contribution of a neuron (filter) to the final loss and iteratively removes those with smaller scores. We describe two variations of our method using the first and second-order Taylor expansions to approximate a filter's contribution. Both methods scale consistently across any network layer without requiring per-layer sensitivity analysis and can be applied to any kind of layer, including skip connections. For modern networks trained on ImageNet, we measured experimentally a high (>93%) correlation between the contribution computed by our methods and a reliable estimate of the true importance. Pruning with the proposed methods led to an improvement over state-of-the-art in terms of accuracy, FLOPs, and parameter reduction. On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0.02% in the top-1 accuracy on ImageNet.

/pdf/importance-estimation-for-neural-network-pruning-1fgf4y1cfs.pdf

Importance Estimation for Neural Network Pruning

Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is L2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.

Loss Functions for Neural Networks for Image Processing

In this paper, we present a novel procedure for the on-the-field autocalibration of triaxial micro accelerometers, which requires neither any equipment nor a controlled environment and allows increasing the accuracy of this kind of microsensor. The procedure exploits the fact that, in static conditions, the modulus of the accelerometer output vector matches that of the gravity acceleration. The calibration model incorporates the bias and scale factor for each axis and the cross-axis symmetrical factors. The parameters are computed through nonlinear optimization, which is solved in a very short time. The calibration procedure was quantitatively tested by comparing the orientation produced by MEMS with that measured by a motion capture system. Results show that the MEMS output, after the calibration procedure, is far more accurate with respect to the output obtained using factory calibration data and almost one order of magnitude more accurate with respect to using traditional calibration models.

Autocalibration of MEMS Accelerometers

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU's computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. Our hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant speed up compared to a CPU implementation; we make it publicly available to other researchers at this https URL .

/pdf/reinforcement-learning-through-asynchronous-advantage-actor-4i1aqo0v86.pdf

Iuri Frosio

Papers

Loss Functions for Image Restoration With Neural Networks

Importance Estimation for Neural Network Pruning

Loss Functions for Neural Networks for Image Processing

Autocalibration of MEMS Accelerometers

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU