Ibtihel Amara

PhD - McGill University

Supervisor

James Clark

Research Topics

Computer Vision

Deep Learning

Natural Language Processing

Representation Learning

GitHub

Publications

What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models

Ahmed Imtiaz Humayun

Ibtihel Amara

Cristina Nader Vasconcelos

Deepak Ramachandran

Candice Schumann

Junfeng He

Katherine A Heller

Golnoosh Farnadi

Negar Rostamzadeh

Mohammad Havaei

Deep Generative Models are frequently used to learn continuous representations of complex data distributions using a finite number of sample… (see more)s. For any generative model, including pre-trained foundation models with GAN, Transformer or Diffusion architectures, generation performance can vary significantly based on which part of the learned data manifold is sampled. In this paper we study the post-training local geometry of the learned manifold and its relationship to generation outcomes for models ranging from toy settings to the latent decoder of the near state-of-the-art Stable Diffusion 1.4 Text-to-Image model. Building on the theory of continuous piecewise-linear (CPWL) generators, we characterize the local geometry in terms of three geometric descriptors - scaling (

2025-01-22

ICLR.cc/2025/Conference (poster)

openreview.net