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Found 14 papers, 2 papers with code
ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models
Although soft prompt tuning is effective in efficiently adapting Vision-Language (V&L) models for downstream tasks, it shows limitations in dealing with distribution shifts.
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
Grounded Language Acquisition From Object and Action Imagery
Deep learning approaches to natural language processing have made great strides in recent years.
Transferable Attack for Semantic Segmentation
We analysis performance of semantic segmentation models wrt.
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Test-time defenses solve these issues but most existing test-time defenses require adapting the model weights, therefore they do not work on frozen models and complicate model memory management.
Adversarial Purification with the Manifold Hypothesis
In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis.
Uncertainty-aware Perception Models for Off-road Autonomous Unmanned Ground Vehicles
We test the hypothesis that model trained on a single dataset may not generalize to other off-road navigation datasets and new locations due to the input distribution drift.
Dropout Inference with Non-Uniform Weight Scaling
Dropout as regularization has been used extensively to prevent overfitting for training neural networks.
On Adversarial Vulnerability of PHM algorithms: An Initial Study
With proliferation of deep learning (DL) applications in diverse domains, vulnerability of DL models to adversarial attacks has become an increasingly interesting research topic in the domains of Computer Vision (CV) and Natural Language Processing (NLP).
Variational Encoder-based Reliable Classification
Machine learning models provide statistically impressive results which might be individually unreliable.
Justification-Based Reliability in Machine Learning
To address this need, we link the question of reliability of a model's individual prediction to the epistemic uncertainty of the model's prediction.
Design of intentional backdoors in sequential models
Recent work has demonstrated robust mechanisms by which attacks can be orchestrated on machine learning models.
Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks
Complex autonomous control systems are subjected to sensor failures, cyber-attacks, sensor noise, communication channel failures, etc.
