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Found 3 papers, 1 papers with code
Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs).
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Fine-Grained Image Classification
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Fine-Grained Image Classification
Fine-Grained Visual Recognition
+4
A Benchmark on Tricks for Large-scale Image Retrieval
Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval.
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
Our classification results on Multi-PIE dataset for facial expression recognition and CIFAR-10 dataset for object classification reveal that the compact CNN with the proposed logarithmic filter grouping scheme outperforms the same network with the uniform filter grouping in terms of accuracy and parameter efficiency.
