Pros and cons of GAN evaluation measures: New developments
A Borji - Computer Vision and Image Understanding, 2022 - Elsevier
This work is an update of my previous paper on the same topic published a few years ago
(Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative …
(Borji, 2019). With the dramatic progress in generative modeling, a suite of new quantitative …
Denoising diffusion implicit models
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image
generation without adversarial training, yet they require simulating a Markov chain for many …
generation without adversarial training, yet they require simulating a Markov chain for many …
Understanding deep learning (still) requires rethinking generalization
Despite their massive size, successful deep artificial neural networks can exhibit a
remarkably small gap between training and test performance. Conventional wisdom …
remarkably small gap between training and test performance. Conventional wisdom …
Automated and autonomous experiments in electron and scanning probe microscopy
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …
part of physics research, with domain applications ranging from theory and materials …
How generative AI models such as ChatGPT can be (mis) used in SPC practice, education, and research? An exploratory study
Abstract Generative Artificial Intelligence (AI) models such as OpenAI's ChatGPT have the
potential to revolutionize Statistical Process Control (SPC) practice, learning, and research …
potential to revolutionize Statistical Process Control (SPC) practice, learning, and research …
Trojdiff: Trojan attacks on diffusion models with diverse targets
Diffusion models have achieved great success in a range of tasks, such as image synthesis
and molecule design. As such successes hinge on large-scale training data collected from …
and molecule design. As such successes hinge on large-scale training data collected from …
Convergence of denoising diffusion models under the manifold hypothesis
V De Bortoli - arxiv preprint arxiv:2208.05314, 2022 - arxiv.org
Denoising diffusion models are a recent class of generative models exhibiting state-of-the-
art performance in image and audio synthesis. Such models approximate the time-reversal …
art performance in image and audio synthesis. Such models approximate the time-reversal …
Generated faces in the wild: Quantitative comparison of stable diffusion, midjourney and dall-e 2
A Borji - arxiv preprint arxiv:2210.00586, 2022 - arxiv.org
The field of image synthesis has made great strides in the last couple of years. Recent
models are capable of generating images with astonishing quality. Fine-grained evaluation …
models are capable of generating images with astonishing quality. Fine-grained evaluation …
Supporting human-ai collaboration in auditing llms with llms
Large language models (LLMs) are increasingly becoming all-powerful and pervasive via
deployment in sociotechnical systems. Yet these language models, be it for classification or …
deployment in sociotechnical systems. Yet these language models, be it for classification or …
Iti-gen: Inclusive text-to-image generation
Text-to-image generative models often reflect the biases of the training data, leading to
unequal representations of underrepresented groups. This study investigates inclusive text …
unequal representations of underrepresented groups. This study investigates inclusive text …