2 citations · 6 across the 6 of their papers we have counts for
7 papers · 1 filter
Quantized Feature Distillation for Network Quantization
Ke Zhu, Yin-Yin He, Jianxin Wu
Neural network quantization aims to accelerate and trim full-precision neural network models by using low bit approximations. Methods adopting the quantization aware training (QAT)…
Instance-based Max-margin for Practical Few-shot Recognition
Minghao Fu, Ke Zhu, Jianxin Wu
In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to real-world applications, this paper proposes a practical FSL (pFSL) setting. pFSL is ba…
No One Left Behind: Improving the Worst Categories in Long-Tailed Learning
Yingxiao Du, Jianxin Wu
Unlike the case when using a balanced training dataset, the per-class recall (i.e., accuracy) of neural networks trained with an imbalanced dataset are known to vary a lot from cat…
Synergistic Self-supervised and Quantization Learning
Yun-Hao Cao, Peiqin Sun, Yechang Huang +2
With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream t…
Worst Case Matters for Few-Shot Recognition
Minghao Fu, Yun-Hao Cao, Jianxin Wu
Few-shot recognition learns a recognition model with very few (e.g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over…
R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning
Guo-Hua Wang, Jianxin Wu
Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parame…