29 citations · 29 across the 2 of their papers we have counts for
3 papers
cs.CV2024
Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization
Avraham Chapman, Haiming Xu, Lingqiao Liu
Training a fine-grained image recognition model with limited data presents a significant challenge, as the subtle differences between categories may not be easily discernible amids…
cs.CV2023
On Interpretable Approaches to Cluster, Classify and Represent Multi-Subspace Data via Minimum Lossy Coding Length based on Rate-Distortion Theory
Kai-Liang Lu, Avraham Chapman
To cluster, classify and represent are three fundamental objectives of learning from high-dimensional data with intrinsic structure. To this end, this paper introduces three interp…
cs.CV2022★ 29 cited
Regularizing Neural Network Training via Identity-wise Discriminative Feature Suppression
Avraham Chapman, Lingqiao Liu
It is well-known that a deep neural network has a strong fitting capability and can easily achieve a low training error even with randomly assigned class labels. When the number of…