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Diffusion Models (DMs) have evolved into advanced image generation tools, especially for few-shot fine-tuning where a pretrained DM is …

In this paper, we propose a novel DNN-based method for supervised causal learning that addresses systematic biases in existing methods, with a newly designed pairwise encoder serving as the core architecture.

in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs.

In this paper, we propose Information Bound as a metric of the amount of information in Bayesian neural networks. Different from mutual information on deterministic neural networks where modification of network structure or specific input data is usually necessary, Information Bound can be easily estimated on current Bayesian neural networks without any modification of network structures or training processes.

in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs.

In this paper, we argue that the randomness of sampling in Bayesian neural networks causes errors in the updating of model parameters during training and some sampled models with poor performance in testing. We propose to train Bayesian neural networks with Adversarial Distribution as a theoretical solution. We further present the Adversarial Sampling method as an approximation in practice.

We propose Spectral Expectation Bound Regularization (SEBR) to enhance the robustness of Bayesian neural networks. Our theoretical analysis reveals that training with SEBR improves the robustness to adversarial noises. We also prove that training with SEBR can reduce the epistemic uncertainty of the model.