AI ML DL

AI ML DL

Solving the ‘Profile View Famine’ With Generative Adversarial Networks

It’s hard to guess what people look like from the side if you only have frontal views of their face; and the chronic lack of profile views in popular datasets makes this a stubborn data problem that’s standing in the way of 360-degree facial synthesis. Now, researchers from Korea are offering a method that might alleviate this traditional roadblock.

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AI ML DL

Repairing Demographic Imbalance in Face Datasets With StyleGAN3

New research from France and Switzerland uses Generative Adversarial Networks (GANs) to create extra examples of races and genders that are under-represented in historical face datasets, in an effort to offset controversies such as the tendency for facial recognition systems to fail to recognize (or to over-recognize) particular types of people.

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AI ML DL

Stable Diffusion Deepfakes and Stylizations With a Single Image

Getting your face into Stable Diffusion has been a relatively complicated affair since the text-to-image system launched in August of 2022 – but a new offering from China and Singapore proposes a method of embedding your own image into Stable Diffusion with only a single photo, instead of needing to train a model every time.

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AI ML DL

Replacing LoRA With a Generic Style Adapter in Stable Diffusion

For creating personalized Stable Diffusion art, Low Rank Adaption (LoRA) models are all the rage this year. But a new academic offering from China is proposing a system that could bake such elegant functionality directly into Stable Diffusion, without the need to curate data and train models.

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AI ML DL

Detecting Cheapfakes With Deepfakes

You don’t need a powerful GPU to deceive the public – just a mischievous turn of phrase, applied to photos or videos that don’t really support the caption. Now, researchers from Norway are using generative systems such as Stable Diffusion and DALL-E 2 to help root out this kind of ‘cheapfake’ or ‘shallowfake’

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AI ML DL

Mixed Emotions: Compound Facial Expressions Will Be Important in Image Synthesis

New research from Australia investigates novel ways to teach AI systems how to recognize a far greater range of human facial expressions than just the six basic expressions widely-used in the Facial Action Coding System (FACS). Work of this kind is going to be important very soon, when public awe about neural human recreations in movies and TVs evolves to a higher critical standard.

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