1 code implementation • 19 Jul 2023 • Xia Huang, Kai Fong Ernest Chong
To tackle the limitation of entropy maximization, we propose $(\alpha, \beta)$-generalized KL divergence, $\mathcal{D}_{\text{KL}}^{\alpha, \beta}(p\|q)$, which can be used to identify significantly more NC instances.
no code implementations • 1 Jan 2021 • Xia Huang, Kai Fong Ernest Chong
At the heart of our framework is a discriminator that predicts whether an input dataset has maximum Shannon entropy, which shall be used on multiple new datasets $\hat{\mathcal{D}}$ synthesized from $\mathcal{D}$ via the insertion of additional label noise.
no code implementations • 22 Oct 2019 • Xia Huang, Hossein Mousavi, Gemma Roig
In this way, the model only relies on the video frames, and does not need pre-processed optical flows as input.