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Found 11 papers, 5 papers with code
Distilling Diversity and Control in Diffusion Models
Distilled diffusion models suffer from a critical limitation: reduced sample diversity compared to their base counterparts.
Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities
We pit state-of-the-art techniques for removing harmful LLM capabilities against a suite of 5 input-space and 6 model tampering attacks.
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Unlike existing control methods that require a user to specify attributes for each edit direction individually, SliderSpace discovers multiple interpretable and diverse directions simultaneously from a single text prompt.
Art-Free Generative Models: Art Creation Without Graphic Art Knowledge
We explore the question: "How much prior art knowledge is needed to create art?"
Erasing Conceptual Knowledge from Language Models
In this work, we propose Erasure of Language Memory (ELM), an approach for concept-level unlearning built on the principle of matching the distribution defined by an introspective classifier.
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models.
Unified Concept Editing in Diffusion Models
Text-to-image models suffer from various safety issues that may limit their suitability for deployment.
Erasing Concepts from Diffusion Models
We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher.
DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art
In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation.
Computer Vision for Autonomous Vehicles
In this work, we try to implement Image Processing techniques in the area of autonomous vehicles, both indoor and outdoor.
How You See Me
Convolution Neural Networks is one of the most powerful tools in the present era of science.
