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Found 20 papers, 9 papers with code
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection
By training them using a small learning rate for about one epoch, we obtained models that retained the accuracy of the backbone classifier while being unusually resistant to gradient attacks including APGD and FAB-T attacks from the AutoAttack package, which we attributed to gradient masking.
PriViT: Vision Transformers for Fast Private Inference
The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications.
Distributionally Robust Classification on a Data Budget
To our knowledge, this is the first result showing (near) state-of-the-art distributional robustness on limited data budgets.
Identity-Preserving Aging of Face Images via Latent Diffusion Models
The performance of automated face recognition systems is inevitably impacted by the facial aging process.
Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos
Our results show that this framework offers state-of-the-art performance on zero-shot transfer and video-based CLIP for predicting the driver's state on two public datasets.
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural representations.
Caption supervision enables robust learners
Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the model inteprets image-linked texts as ground-truth labels.
Revisiting Self-Distillation
We first show that even with a highly accurate teacher, self-distillation allows a student to surpass the teacher in all cases.
A Meta-Analysis of Distributionally-Robust Models
State-of-the-art image classifiers trained on massive datasets (such as ImageNet) have been shown to be vulnerable to a range of both intentional and incidental distribution shifts.
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers.
Selective Network Linearization for Efficient Private Inference
To reduce PI latency we propose a gradient-based algorithm that selectively linearizes ReLUs while maintaining prediction accuracy.
Adversarial Token Attacks on Vision Transformers
Vision transformers rely on a patch token based self attention mechanism, in contrast to convolutional networks.
Differentiable Spline Approximations
Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer" in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis.
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs).
Differentiable Programming for Piecewise Polynomial Functions
The paradigm of differentiable programming has considerably enhanced the scope of machine learning via the judicious use of gradient-based optimization.
Adversarially Robust Learning via Entropic Regularization
In this paper we propose a new family of algorithms, ATENT, for training adversarially robust deep neural networks.
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements.
ESPN: Extremely Sparse Pruned Networks
Deep neural networks are often highly overparameterized, prohibiting their use in compute-limited systems.
Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions.
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
We propose a novel approach to generate such `semantic' adversarial examples by optimizing a particular adversarial loss over the range-space of a parametric conditional generative model.
