1 code implementation • 6 Feb 2024 • Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
However, most DG methods assume access to abundant source data in the target label space, a requirement that proves overly stringent for numerous real-world applications, where acquiring the same label space as the target task is prohibitively expensive.
1 code implementation • 5 Feb 2024 • Eric Yang Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis, Brian Kulis
We first show that when an OOD data point is misclassified, the correct class can be typically found in the Top-K predicted classes.
1 code implementation • 21 Nov 2023 • Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
A recent study, WaffleCLIP, demonstrated that similar zero-shot accuracy can be achieved with an ensemble of random descriptors.
1 code implementation • 4 Oct 2022 • Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
There is extensive interest in metric learning methods for image retrieval.
1 code implementation • 26 May 2022 • Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution labeled data.
no code implementations • 2 Nov 2021 • Ali Siahkamari, Durmus Alp Emre Acar, Christopher Liao, Kelly Geyer, Venkatesh Saligrama, Brian Kulis
For the task of convex Lipschitz regression, we establish that our proposed algorithm converges with iteration complexity of $ O(n\sqrt{d}/\epsilon)$ for a dataset $\bm X \in \mathbb R^{n\times d}$ and $\epsilon > 0$.