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😎AI/Generative AI11

[Paper Review] Prompt-to-Prompt Image Editing with Cross Attention Control https://arxiv.org/abs/2208.01626 Prompt-to-Prompt Image Editing with Cross Attention ControlRecent large-scale text-driven synthesis models have attracted much attention thanks to their remarkable capabilities of generating highly diverse images that follow given text prompts. Such text-based synthesis methods are particularly appealing to humansarxiv.org 기존 LLI (Large-scale language-image) mo.. 2025. 2. 13.

[Paper Review] 📌Attention Is All You Need (aka. Transformer) 드디어 나오셨습니다. Transformer! 🥁 두둥탁!https://arxiv.org/abs/1706.03762 Attention Is All You NeedThe dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a newarxiv.org "Attention Is All You Need" 논문이 나오게 된 계기는.. 2025. 2. 11.

[Paper Review] Classifier-Free Diffusion Guidance https://arxiv.org/abs/2207.12598 Classifier-Free Diffusion GuidanceClassifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier garxiv.org Introduce 이 논문은 classifier guidance 논문에서 classifier 을 사용하지 않고도 c.. 2025. 2. 5.

[Paper Review] High-Resolution Image Synthesis with Latent Diffusion Models (Aka. Stable Diffusion) https://arxiv.org/abs/2112.10752 High-Resolution Image Synthesis with Latent Diffusion ModelsBy decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism tarxiv.org 이번 주제는 아주 유명한 Stable Diffuion 논문을 리뷰해보도.. 2025. 2. 4.

[코드공부][Deepfake defection] SeqDeepFake 보호되어 있는 글 입니다. 2023. 7. 20.

[Paper Review][Generative AI] SeqDeepFake: Detecting and Recovering Sequential DeepFake Manipulation SeqDeepFake: Detecting and Recovering Sequential DeepFake ManipulationS-Lab, ECCV 2022 이 논문은 딥페이크 기술을 사용한 가짜 영상 조작을 감지하고 복구하는 방법에 대해 깊이 있는 연구를 수행하였습니다. 논문의 주요 목적은 시퀀셜 딥페이크 조작(Detecting Sequential DeepFake Manipulation)을 감지하기 위한 새로운 연구 문제를 제시하는 것입니다. 일반적으로 딥페이크 조작은 단일 단계의 조작을 감지하는 것에 초점을 맞추고 있으나, 최근에는 얼굴 조작 애플리케이션을 통해 다단계 조작이 가능해졌습니다. 이러한 시퀀셜 딥페이크 조작은 기존의 방법으로는 감지하기 어려운 도전적인 문제를 제시합니다. 논문에서는 시퀀.. 2023. 7. 17.

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