6.2k citations · 8.4k across the 16 of their papers we have counts for
19 papers
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
Kai Wang, Zekai Li, Zhi-Qi Cheng +6
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios.…
Understanding the Detrimental Class-level Effects of Data Augmentation
Polina Kirichenko, Mark Ibrahim, Randall Balestriero +4
Data augmentation (DA) encodes invariance and provides implicit regularization critical to a model's performance in image classification tasks. However, while DA improves average a…
Embarassingly Simple Dataset Distillation
Yunzhen Feng, Ramakrishna Vedantam, Julia Kempe
Dataset distillation extracts a small set of synthetic training samples from a large dataset with the goal of achieving competitive performance on test data when trained on this sa…
Improving Selective Visual Question Answering by Learning from Your Peers
Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary +5
Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-…
Hyperbolic Image-Text Representations
Karan Desai, Maximilian Nickel, Tanmay Rajpurohit +2
Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs. Despite being intuitive, current la…
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel +2
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same…