Search Results for author:
Found 11 papers, 9 papers with code
Dataset Size Recovery from LoRA Weights
In this paper, we introduce a new task: dataset size recovery, that aims to determine the number of samples used to train a model, directly from its weights.
Real-Time Deepfake Detection in the Real-World
The image deepfake score is the pooled score of its patches.
On the Origin of Llamas: Model Tree Heritage Recovery
However, this information is underutilized as the weights are uninterpretable, and publicly available models are disorganized.
Distilling Datasets Into Less Than One Image
Current methods frame this as maximizing the distilled classification accuracy for a budget of K distilled images-per-class, where K is a positive integer.
Ranked #1 on
Dataset Distillation - 1IPC
on CUB-200-2011
Recovering the Pre-Fine-Tuning Weights of Generative Models
The dominant paradigm in generative modeling consists of two steps: i) pre-training on a large-scale but unsafe dataset, ii) aligning the pre-trained model with human values via fine-tuning.
Dreamix: Video Diffusion Models are General Video Editors
Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text prompt.
Conffusion: Confidence Intervals for Diffusion Models
Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting.
Anomaly Detection Requires Better Representations
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry.
Ranked #1 on
Anomaly Detection
on ODDS
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity.
Ranked #3 on
3D Anomaly Detection and Segmentation
on MVTEC 3D-AD
3D Anomaly Detection
3D Anomaly Detection and Segmentation
+3
Image Shape Manipulation from a Single Augmented Training Sample
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image.
Ranked #1 on
Image Manipulation
on LRS2
Image Shape Manipulation from a Single Augmented Training Sample
In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image.
