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Found 22 papers, 12 papers with code
Combating Missing Modalities in Egocentric Videos at Test Time
Understanding videos that contain multiple modalities is crucial, especially in egocentric videos, where combining various sensory inputs significantly improves tasks like action recognition and moment localization.
Revisiting Test Time Adaptation under Online Evaluation
To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.
Online Distillation with Continual Learning for Cyclic Domain Shifts
In this paper, we propose a solution to this issue by leveraging the power of continual learning methods to reduce the impact of domain shifts.
Real-Time Evaluation in Online Continual Learning: A New Hope
We show that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings.
PIVOT: Prompting for Video Continual Learning
In this paper, we address the problem of continual learning for video data.
SimCS: Simulation for Domain Incremental Online Continual Segmentation
This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries.
Generalizability of Adversarial Robustness Under Distribution Shifts
Recent progress in empirical and certified robustness promises to deliver reliable and deployable Deep Neural Networks (DNNs).
Certified Robustness in Federated Learning
However, as in the single-node supervised learning setup, models trained in federated learning suffer from vulnerability to imperceptible input transformations known as adversarial attacks, questioning their deployment in security-related applications.
3DeformRS: Certifying Spatial Deformations on Point Clouds
In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations.
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input.
On the Robustness of Quality Measures for GANs
Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception.
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks.
ANCER: Anisotropic Certification via Sample-wise Volume Maximization
Randomized smoothing has recently emerged as an effective tool that enables certification of deep neural network classifiers at scale.
DeformRS: Certifying Input Deformations with Randomized Smoothing
Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e. g. translations, rotations, etc.
Combating Adversaries with Anti-Adversaries
Deep neural networks are vulnerable to small input perturbations known as adversarial attacks.
On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective
This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations.
Data-Dependent Randomized Smoothing
In this work, we revisit Gaussian randomized smoothing and show that the variance of the Gaussian distribution can be optimized at each input so as to maximize the certification radius for the construction of the smooth classifier.
Rethinking Clustering for Robustness
This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness.
Adaptive Learning of the Optimal Batch Size of SGD
Recent advances in the theoretical understanding of SGD led to a formula for the optimal batch size minimizing the number of effective data passes, i. e., the number of iterations times the batch size.
On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective
Our main finding is that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes.
Gabor Layers Enhance Network Robustness
We revisit the benefits of merging classical vision concepts with deep learning models.
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective
We use tropical geometry, a new development in the area of algebraic geometry, to provide a characterization of the decision boundaries of a simple neural network of the form (Affine, ReLU, Affine).
