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Found 15 papers, 8 papers with code
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Exploiting Style Latent Flows for Generalizing Deepfake Detection Video Detection
This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos.
One-Shot Structure-Aware Stylized Image Synthesis
While GAN-based models have been successful in image stylization tasks, they often struggle with structure preservation while stylizing a wide range of input images.
Noise Map Guidance: Inversion with Spatial Context for Real Image Editing
Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images.
Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Addressing the limitations of text as a source of accurate layout representation in text-conditional diffusion models, many works incorporate additional signals to condition certain attributes within a generated image.
Scaling of Class-wise Training Losses for Post-hoc Calibration
The class-wise training losses often diverge as a result of the various levels of intra-class and inter-class appearance variation, and we find that the diverging class-wise training losses cause the uncalibrated prediction with its reliability.
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations
For experiments, we design new test scenarios varying from the training settings in GAN models, color manipulations, and object categories.
Self-supervised GAN Detector
Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news.
MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
When images are recaptured on display screens, various patterns differing by the screens as known as the moir\'e patterns can be also captured in spoof images.
FICGAN: Facial Identity Controllable GAN for De-identification
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility.
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of those images.
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image.
Toward Spatially Unbiased Generative Models
From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased.
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets
We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games.
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders
StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level strategic decision making.
