The researchers suggest that the current crop of machine learning architectures may be inferring something far more fundamental (or, at least, unexpected) from images than was previously thought, and that ‘nonsense' images can potentially impart a great deal of this knowledge far more cheaply, even with the possible use of ad hoc synthetic data, via dataset-generation architectures that generate random images at training time: ‘In this paper, we go a step further and ask if we can do away with real image datasets entirely, by learning from procedural noise processes.' To counter these costs, interest has surged in learning from cheaper data sources, such as unlabeled images. ‘Current vision systems are trained on huge datasets, and these datasets come with costs: curation is expensive, they inherit human biases, and there are concerns over privacy and usage rights. The results obtained by the researchers call into question the fundamental relationship between image-based neural networks and the ‘real world' images that are thrown at them in alarmingly greater volumes each year, and imply that the need to obtain, curate and otherwise wrangle hyperscale image datasets may eventually become redundant. The project page for the initiative lets you interactively view the different types of random image datasets used in the experiment. This suggests (and it’s a bit of a revolutionary notion) a new type of ‘under-fitting', where ‘diversity' trumps ‘naturalism'. Neither can these results be attributed to over-fitting: a lively discussion between the authors and reviewers at Open Review reveals that mixing different content from visually diverse datasets (such as ‘dead leaves', ‘fractals' and ‘procedural noise' – see image below) into a training dataset actually improves accuracy in these experiments. Rather, it means that the CSAIL models have derived broadly applicable central ‘truths' from image data so apparently unstructured that it should not be capable of supplying it. In this sense ‘accuracy' does not mean that a result necessarily looks like a face, a church, a pizza, or any other particular domain for which you might be interested in creating an image synthesis system, such as a Generative Adversarial Network, or an encoder/decoder framework. While the ‘random noise' datasets preceding it (pictured in various colors, see index top-left) can't match that, they are nearly all within respectable upper and lower bounds (red dashed lines) for accuracy. On the far right of the image above, the black columns represent accuracy scores (on Imagenet-100) for four ‘real' datasets. Source: įeeding apparent ‘visual trash' into popular computer vision architectures should not result in this kind of performance.
#Random images full#
See the Pen zaaXEZ by Andreas Wik ( on CodePen.įor a full list of features – check out the Unsplash Source documentation.Generative models from the experiment, sorted by performance. Here’s a simple CodePen I put together illustrating how you could use this dynamically with JavaScript: You place the search terms at the end of the URL, so before you could add the size if you’d like:
Let’s search for city and night (so fkn creative): You can generate images from search terms.
Let’s generate a random image with the width and height of 300px: There’s also an option to set the size of the image you want generated. The URL format would be like so:Ĭlick this link below to generate a random image from the user wsanter: We can also generate a random image from a specific user. Here’s an example, generating a completely random image from their massive storage: While they do have a great API for developers, they also give you the option to simply access random images via URL’s. I use it myself quite often, for large background images.
#Random images free#
In case you haven’t heard already – Unsplash is the place to go when you need royalty free photos to use in your projects, whether it’s for commercial use or not. Generate Random Images From Unsplash Without Using The API
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