27 citations · 63 across the 5 of their papers we have counts for
5 papers
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion
Joshua Kazdan, Hao Sun, Jiaqi Han +2
Diffusion models have a tendency to exactly replicate their training data, especially when trained on small datasets. Most prior work has sought to mitigate this problem by imposin…
Efficient Poverty Mapping using Deep Reinforcement Learning
Kumar Ayush, Burak Uzkent, Kumar Tanmay +3
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure meas…
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Sharon Zhou, Eric Zelikman, Fred Lu +3
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring…
Fair Generative Modeling via Weak Supervision
Kristy Choi, Aditya Grover, Trisha Singh +2
Real-world datasets are often biased with respect to key demographic factors such as race and gender. Due to the latent nature of the underlying factors, detecting and mitigating b…
Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover +2
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and chan…