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A Unified Mixture-View Framework for Unsupervised Representation Learning
Xiangxiang Chu, Xiaohang Zhan, Bo Zhang
Recent unsupervised contrastive representation learning follows a Single Instance Multi-view (SIM) paradigm where positive pairs are usually constructed with intra-image data augme…
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation
Xiaoxing Wang, Xiangxiang Chu, Yuda Fan +4
Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet re…
AutoKWS: Keyword Spotting with Differentiable Architecture Search
Bo Zhang, Wenfeng Li, Qingyuan Li +3
Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption. It is however challenging to design models that have both high accur…
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators
Xiangxiang Chu, Xiaoxing Wang, Bo Zhang +3
Despite the fast development of differentiable architecture search (DARTS), it suffers from long-standing performance instability, which extremely limits its application. Existing…
Noisy Differentiable Architecture Search
Xiangxiang Chu, Bo Zhang
Simplicity is the ultimate sophistication. Differentiable Architecture Search (DARTS) has now become one of the mainstream paradigms of neural architecture search. However, it larg…
MixPath: A Unified Approach for One-shot Neural Architecture Search
Xiangxiang Chu, Shun Lu, Xudong Li +1
Blending multiple convolutional kernels is proved advantageous in neural architecture design. However, current two-stage neural architecture search methods are mainly limited to si…