Efficient Deep Embedded Subspace Clustering Jinyu Cai1,3, Jicong Fan2,3∗, Wenzhong Guo1, Shiping Wang1, Yunhe Zhang1, Zhao Zhang4 1College of Computer and Data Science, Fuzhou University, China 2School of Data
The Efficient Deep Embedded Subspace Clustering (EDESC) (Cai et al., 2022) initializes the base proxy for each subspace using the K-means clustering results of the first phase. Then it trains the network by minimizing the KL divergence between the soft and the target cluster assignments, the...
Embedded-Neural-Network model_compression model-compression (in Chinese) Efficient-Segmentation-Networks AutoML NAS Literature Papers with code ImageNet Benckmark Self-supervised ImageNet Benckmark NVIDIA Blog with Sparsity TagAbout Collection of recent methods on (deep) neural network compression and acc...
CMLL optimizes the correlation between the embedded tag spaces and features while minimizing the loss in retrieving tag space. Multi-label learning algorithms have substantial difficulties arising from the high-dimensional feature space and the presence of noise in multi-label datasets. Feature selection...
(Analysis of Variance). The wrapper method is based on the performance of the model on the dataset. The wrapper method includes Forward Selection, Backward Elimination, and Exhaustive Feature Selection techniques. The embedded approach combines the Filter and the Wrapper method by performing feature ...
BERTScore [41] (BERT-based Sentence Score) is a context-embedded evaluation metric based on the pre-trained BERT model. It firstly encodes the input text sequence through the BERT model to obtain a context-aware character embedding representation. For each word in the generated text, the word...