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Found 6 papers, 2 papers with code
Exploring Safety Generalization Challenges of Large Language Models via Code
The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs.
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond
We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models.
Ranked #6 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research.
Adversarial Robustness via Adaptive Label Smoothing
We first show both theoretically and empirically that strong smoothing in AT increases local smoothness of the loss surface which is beneficial for robustness but sacrifices the training loss which influences the accuracy of samples near the decision boundary.
