Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection.

The Rashomon effect is a mixed blessing in responsible machine learning.

This highlights the importance of selecting an appropriate schedule for each dataset.

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness.

Machine learning craves high-quality data which is a major bottleneck during realistic deployment, as it takes abundant resources and massive human labor to collect and label data. Unfortunately, label noise where image data mismatches with incorrect label exists ubiquitously in all kinds of datasets, significantly degrading the learning performance of deep networks. Learning with Label Noise (LNL) has been a common strategy for mitigating the influence of noisy labels.

The TSCI approach is model-agnostic and can be used as a drop-in replacement for CCM and its generalizations.

Instead of relying on the LLM itself to unlearn, we enforce an unlearned state during inference by employing a prompt classifier to identify and safeguard prompts to forget.

We study thorax disease classification in this paper.

Model Parsing defines the task of predicting hyperparameters of the generative model (GM), given a GM-generated image as the input.