Search Results for author:
Found 15 papers, 7 papers with code
Customizing Text-to-Image Diffusion with Camera Viewpoint Control
Model customization introduces new concepts to existing text-to-image models, enabling the generation of the new concept in novel contexts.
Ablating Concepts in Text-to-Image Diffusion Models
To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i. e., preventing the generation of a target concept.
Multi-Concept Customization of Text-to-Image Diffusion
Can we teach a model to quickly acquire a new concept, given a few examples?
Content-Based Search for Deep Generative Models
To address this need, we introduce the task of content-based model search: given a query and a large set of generative models, finding the models that best match the query.
Ranked #1 on
Model Description Based Search
on Generative Models
Contrastive Learning
Image and Sketch based Model Retrieval
+4
Ensembling Off-the-shelf Models for GAN Training
Can the collective "knowledge" from a large bank of pretrained vision models be leveraged to improve GAN training?
Ranked #1 on
Image Generation
on AFHQ Cat
Data InStance Prior (DISP) in Generative Adversarial Networks
Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques.
LT-GAN: Self-Supervised GAN with Latent Transformation Detection
We propose a self-supervised approach (LT-GAN) to improve the generation quality and diversity of images by estimating the GAN-induced transformation (i. e. transformation induced in the generated images by perturbing the latent space of generator).
Ranked #4 on
Image Generation
on CelebA-HQ 128x128
Data Instance Prior for Transfer Learning in GANs
Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images.
On the Benefits of Models with Perceptually-Aligned Gradients
In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
ShapeVis: High-dimensional Data Visualization at Scale
This graph captures the structural characteristics of the point cloud, and its weights are determined using a Finite Markov Chain.
A Method for Computing Class-wise Universal Adversarial Perturbations
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks.
Attributional Robustness Training using Input-Gradient Spatial Alignment
Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Error Rate metric)
BIG-bench Machine Learning
Weakly-Supervised Object Localization
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers.
ReDecode Framework for Iterative Improvement in Paraphrase Generation
Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP.
