1.3k citations · 1.5k across the 16 of their papers we have counts for
16 papers
Distributionally Robust Post-hoc Classifiers under Prior Shifts
Jiaheng Wei, Harikrishna Narasimhan, Ehsan Amid +3
The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution. We investigate the problem of tr…
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo +3
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separa…
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
Kushagra Pandey, Avideep Mukherjee, Piyush Rai +1
Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable late…
Solving Inverse Problems with NerfGANs
Giannis Daras, Wen-Sheng Chu, Abhishek Kumar +2
We introduce a novel framework for solving inverse problems using NeRF-style generative models. We are interested in the problem of 3-D scene reconstruction given a single 2-D imag…
Constrained Instance and Class Reweighting for Robust Learning under Label Noise
Abhishek Kumar, Ehsan Amid
Deep neural networks have shown impressive performance in supervised learning, enabled by their ability to fit well to the provided training data. However, their performance is lar…
Retrieve in Style: Unsupervised Facial Feature Transfer and Retrieval
Min Jin Chong, Wen-Sheng Chu, Abhishek Kumar +1
We present Retrieve in Style (RIS), an unsupervised framework for facial feature transfer and retrieval on real images. Recent work shows capabilities of transferring local facial…