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High quality image resizing filters – Lanczos and Bicubic for best compression quality, and Linear for speedĬreate e-mail friendly images or use them for internet publishing High quality results using ultra fast multi-core, photo resize technology Resize pictures, resize photos and resize images – compress, convert, and easily create copies of your pictures Use the live preview to see what the final result will look like before actually processing the image. Customize pictures by converting them to sepia or add a border.

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Using this free resize image software, you can create e-mail friendly versions of your images, load them faster, move them easily from folder to folder, change their format, edit large numbers of image files/batch image resize and thus save space on your hard drive.Īdd your own watermark to protect your work or add a copyright to photos with html-like text formatting and transparency support.

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Light Image Resizer is integrated into the Windows explorer shell, right click on your pictures and start working on your pictures!

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It is the perfect tool for those who store their digital pictures and images on their PC and who want to resize, compress, convert, create copies, import or organize photos. We anticipate this study to be a basis for further improvement of deep neural networks’ OoD generalization performance, which is highly demanded to achieve safe and fair AI applications.Light Image Resizer from Obvious Idea (previously known as VSO Image Resizer) is a free tool that organizes your photos by shrinking their resolution or moving them within your hard drive. Finally, we demonstrate that even though the three approaches focus on different aspects of DNNs, they all tend to lead to the same underlying neural mechanism to enable OoD accuracy gains - individual neurons in the intermediate layers become invariant to OoD orientations and illuminations. These datasets allow to study the effects of different amounts of bias and are challenging as DNNs perform poorly in OoD conditions. We report results in four datasets: two datasets are modified from the MNIST and iLab datasets, and the other two are novel (one of 3D rendered cars and another of objects taken from various controlled orientations and illumination conditions). Each of these approaches substantially improves the DNN’s OoD accuracy (more than 20% in some cases). Namely, these approaches are (i) training much longer after convergence of the in-distribution (InD) validation accuracy, i.e., late-stopping, (ii) tuning the momentum parameter of the batch normalization layers, and (iii) enforcing invariance of the neural activity in an intermediate layer to orientation and illumination conditions.

In this paper, we investigate three different approaches that lead to the emergence of invariant neurons and substantially improve DNNs in recognizing objects in OoD orientations and illuminations.

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Neurons that are invariant to orientations and illuminations have been proposed as a neural mechanism that could facilitate OoD generalization, but it is unclear how to encourage the emergence of such invariant neurons. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. The training data distribution is often biased towards objects in certain orientations and illumination conditions.