SNIPER: Efficient Multi-Scale Training
1、Introduction SNIP採用圖像金字塔的方式需要對每一個像素進行處理，就會導致運行速遞慢，SNIPER則對次進行了改進，而是以適當的比例處理gt（稱爲chips）周圍的上下文區域，在訓練期間每個圖像生成的chips的數量會根據場景複雜度而自適應地變化，由於SNIPER在衝採樣後的低分辨率的chips上運行，故其可以在訓練期間收益於Batch Normalization，而不需要在GP
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相關文章
- 1. SNIPER: Efficient Multi-Scale Training
- 2. SNIPER- Efficient Multi-Scale Training
- 3. SNIPER
- 4. 【SNIPER】
- 5. 物體檢測之SNIPER
- 6. HIERARCHICAL MULTISCALE RECURRENT NEURAL NETWORKS
- 7. 讀 Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- 8. 【論文精讀】Select Via Proxy: Efficient Data Selection For Training DeepNetworks
- 9. 論文閱讀——Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- 10. Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions筆記