528 citations · 4.4k across the 100 of their papers we have counts for
21 papers · 1 filter
What do Vision Transformers Learn? A Visual Exploration
Amin Ghiasi, Hamid Kazemi, Eitan Borgnia +5
Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing…
Learning Multimodal Data Augmentation in Feature Space
Zichang Liu, Zhiqiang Tang, Xingjian Shi +4
The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances…
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers
Wanqian Yang, Polina Kirichenko, Micah Goldblum +1
Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior be…
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization
Sanae Lotfi, Marc Finzi, Sanyam Kapoor +3
While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works. In this…
K-SAM: Sharpness-Aware Minimization at the Speed of SGD
Renkun Ni, Ping-yeh Chiang, Jonas Geiping +3
Sharpness-Aware Minimization (SAM) has recently emerged as a robust technique for improving the accuracy of deep neural networks. However, SAM incurs a high computational cost in p…
On Feature Learning in the Presence of Spurious Correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver +1
Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the lear…