Tackling Structural Hallucination in Image Translation with Local Diffusion

1 code implementation • 9 Apr 2024 • Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander

We hypothesize such hallucinations result from local OOD regions in the conditional images.

Hallucination Image Generation +1

An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning

2 code implementations • 18 Oct 2023 • Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare

Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images.

Image Generation Sentence

Unlocking the Heart Using Adaptive Locked Agnostic Networks

no code implementations • 21 Sep 2023 • Sylwia Majchrowska, Anders Hildeman, Philip Teare, Tom Diethe

Supervised training of deep learning models for medical imaging applications requires a significant amount of labeled data.

Classification of datasets with imputed missing values: does imputation quality matter?

no code implementations • 16 Jun 2022 • Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro Lio, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.

BIG-bench Machine Learning Imputation +1