18 research outputs found
SimCS: simulation for domain incremental online continual segmentation
Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. 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. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods
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. We use tropical geometry, a new development in the area of algebraic geometry, to characterize the decision boundaries of a simple network of the form (Affine, ReLU, Affine). 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. The generators of these zonotopes are functions of the network parameters. This geometric characterization provides new perspectives to three tasks. (i) We propose a new tropical perspective to the lottery ticket hypothesis, where we view the effect of different initializations on the tropical geometric representation of a network's decision boundaries. (ii) Moreover, we propose new tropical based optimization reformulations that directly influence the decision boundaries of the network for the task of network pruning. (iii) At last, we discuss the reformulation of the generation of adversarial attacks in a tropical sense. We demonstrate that one can construct adversaries in a new tropical setting by perturbing a specific set of decision boundaries by perturbing a set of parameters in the network
Data Dependent Randomized Smoothing
Randomized smoothing is a recent technique that achieves state-of-art
performance in training certifiably robust deep neural networks. While the
smoothing family of distributions is often connected to the choice of the norm
used for certification, the parameters of these distributions are always set as
global hyper parameters independent of the input data on which a network is
certified. 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 smoothed
classifier. This new approach is generic, parameter-free, and easy to
implement. In fact, we show that our data dependent framework can be seamlessly
incorporated into 3 randomized smoothing approaches, leading to consistent
improved certified accuracy. When this framework is used in the training
routine of these approaches followed by a data dependent certification, we
achieve 9\% and 6\% improvement over the certified accuracy of the strongest
baseline for a radius of 0.5 on CIFAR10 and ImageNet.Comment: First two authors contributed equally to this wor
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. We use tropical geometry, a new development in the area of
algebraic geometry, to characterize the decision boundaries of a simple network
of the form (Affine, ReLU, Affine). 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. The generators of
these zonotopes are functions of the network parameters. This geometric
characterization provides new perspectives to three tasks. (i) We propose a new
tropical perspective to the lottery ticket hypothesis, where we view the effect
of different initializations on the tropical geometric representation of a
network's decision boundaries. (ii) Moreover, we propose new tropical based
optimization reformulations that directly influence the decision boundaries of
the network for the task of network pruning. (iii) At last, we discuss the
reformulation of the generation of adversarial attacks in a tropical sense. We
demonstrate that one can construct adversaries in a new tropical setting by
perturbing a specific set of decision boundaries by perturbing a set of
parameters in the network.Comment: First two authors contributed equally to this wor
Generalizability of Adversarial Robustness Under Distribution Shifts
Recent progress in empirical and certified robustness promises to deliver
reliable and deployable Deep Neural Networks (DNNs). Despite that success, most
existing evaluations of DNN robustness have been done on images sampled from
the same distribution that the model was trained on. Yet, in the real world,
DNNs may be deployed in dynamic environments that exhibit significant
distribution shifts. In this work, we take a first step towards thoroughly
investigating the interplay between empirical and certified adversarial
robustness on one hand and domain generalization on another. To do so, we train
robust models on multiple domains and evaluate their accuracy and robustness on
an unseen domain. We observe that: (1) both empirical and certified robustness
generalize to unseen domains, and (2) the level of generalizability does not
correlate well with input visual similarity, measured by the FID between source
and target domains. We also extend our study to cover a real-world medical
application, in which adversarial augmentation enhances both the robustness and
generalization accuracy in unseen domains
SimCS: Simulation for Domain Incremental Online Continual Segmentation
Continual Learning is a step towards lifelong intelligence where models
continuously learn from recently collected data without forgetting previous
knowledge. Existing continual learning approaches mostly focus on image
classification in the class-incremental setup with clear task boundaries and
unlimited computational budget. 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.
ODICS arises in many practical applications. In autonomous driving, this may
correspond to the realistic scenario of training a segmentation model over time
on a sequence of cities. We analyze several existing continual learning methods
and show that they perform poorly in this setting despite working well in
class-incremental segmentation. We propose SimCS, a parameter-free method
complementary to existing ones that uses simulated data to regularize continual
learning. Experiments show that SimCS provides consistent improvements when
combined with different CL methods.Comment: Accepted to AAAI Conference on Artificial Intelligence (AAAI'24