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03/23/2023
Ablating Concepts in Text-to-Image Diffusion Models
Large-scale text-to-image diffusion models can generate high-fidelity im...
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12/08/2022
Multi-Concept Customization of Text-to-Image Diffusion
While generative models produce high-quality images of concepts learned ...
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10/06/2022
Content-Based Search for Deep Generative Models
The growing proliferation of pretrained generative models has made it in...
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12/16/2021
Ensembling Off-the-shelf Models for GAN Training
The advent of large-scale training has produced a cornucopia of powerful...
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12/08/2020
Data Instance Prior for Transfer Learning in GANs
Recent advances in generative adversarial networks (GANs) have shown rem...
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10/19/2020
LT-GAN: Self-Supervised GAN with Latent Transformation Detection
Generative Adversarial Networks (GANs) coupled with self-supervised task...
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05/04/2020
On the Benefits of Models with Perceptually-Aligned Gradients
Adversarial robust models have been shown to learn more robust and inter...
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01/15/2020
ShapeVis: High-dimensional Data Visualization at Scale
We present ShapeVis, a scalable visualization technique for point cloud ...
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12/01/2019
A Method for Computing Class-wise Universal Adversarial Perturbations
We present an algorithm for computing class-specific universal adversari...
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11/29/2019
On the Benefits of Attributional Robustness
Interpretability is an emerging area of research in trustworthy machine ...
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07/28/2019
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
Few-shot learning algorithms aim to learn model parameters capable of ad...
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05/13/2019
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
Neural networks are vulnerable to adversarial attacks -- small visually ...
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11/11/2018
