47 citations · 78 across the 10 of their papers we have counts for
10 papers
Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation
Haoqi Wang, Zhizhong Li, Wayne Zhang
We generalize the class vectors found in neural networks to linear subspaces (i.e.~points in the Grassmann manifold) and show that the Grassmann Class Representation (GCR) enables…
Collaborative Anomaly Detection
Ke Bai, Aonan Zhang, Zhizhong Li +3
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item. This can be formula…
Class-Incremental Learning with Strong Pre-trained Models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li +4
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world…
ViM: Out-Of-Distribution with Virtual-logit Matching
Haoqi Wang, Zhizhong Li, Litong Feng +1
Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability. However, the immense diversit…
Learning Curves for Analysis of Deep Networks
Derek Hoiem, Tanmay Gupta, Zhizhong Li +1
Learning curves model a classifier's test error as a function of the number of training samples. Prior works show that learning curves can be used to select model parameters and ex…
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
Hongxu Yin, Pavlo Molchanov, Zhizhong Li +5
We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network. We 'invert' a trained network (teacher) to synthes…
Task-Assisted Domain Adaptation with Anchor Tasks
Zhizhong Li, Linjie Luo, Sergey Tulyakov +2
Some tasks, such as surface normals or single-view depth estimation, require per-pixel ground truth that is difficult to obtain on real images but easy to obtain on synthetic. Howe…
A Dual-level Model Predictive Control Scheme for Multi-timescale Dynamical Systems--Extended Version
Xinglong Zhang, Wei Jiang, Shuyou Yu +2
So far, many control algorithms have been developed for singularly perturbed systems. However, in many industrial processes, enforcing closed-loop fast-slow dynamics for peculiarly…
Complete 3D Scene Parsing from an RGBD Image
Chuhang Zou, Ruiqi Guo, Zhizhong Li +1
One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our…
Learning without Forgetting
Zhizhong Li, Derek Hoiem
When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as th…