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Found 6 papers, 4 papers with code
Filtered-Guided Diffusion: Fast Filter Guidance for Black-Box Diffusion Models
In this work, we show that much of this low-level control can be achieved without additional training or any access to features of the diffusion model.
FactorMatte: Redefining Video Matting for Re-Composition Tasks
Based on this observation, we present a method for solving the factor matting problem that produces useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections.
Robotic Dough Shaping
The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically.
Measuring Dataset Granularity
We also investigate the interplay between dataset granularity with a variety of factors and find that fine-grained datasets are more difficult to learn from, more difficult to transfer to, more difficult to perform few-shot learning with, and more vulnerable to adversarial attacks.
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
We show that we can select a layer of the source model to perturb without any knowledge of the target models while achieving high transferability.
Intermediate Level Adversarial Attack for Enhanced Transferability
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models.
