4k citations · 16.1k across the 265 of their papers we have counts for
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Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping
Prakhar Kaushik, Alex Gain, Adam Kortylewski +1
Catastrophic forgetting in neural networks is a significant problem for continual learning. A majority of the current methods replay previous data during training, which violates t…
Smooth Adversarial Training
Cihang Xie, Mingxing Tan, Boqing Gong +2
It is commonly believed that networks cannot be both accurate and robust, that gaining robustness means losing accuracy. It is also generally believed that, unless making networks…
Label Distribution Learning Forests
Wei Shen, Kai Zhao, Yilu Guo +1
Label distribution learning (LDL) is a general learning framework, which assigns to an instance a distribution over a set of labels rather than a single label or multiple labels. C…
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
Junhua Mao, Jiajing Xu, Yushi Jing +1
In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 m…
Unsupervised learning of object semantic parts from internal states of CNNs by population encoding
Jianyu Wang, Zhishuai Zhang, Cihang Xie +2
We address the key question of how object part representations can be found from the internal states of CNNs that are trained for high-level tasks, such as object classification. T…
Learning Deep Structured Models
Liang-Chieh Chen, Alexander G. Schwing, Alan L. Yuille +1
Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to e…