Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.

TL;DR: A novel hybrid Bees Regression Convolutional Neural Network (BA-RCNN) is used to predict porosity in Selective Laser Melting (SLM) parts. The novel approach yielded a higher accuracy than the existing image binarization method.

TL;DR: AnoLeaf as mentioned in this paper proposes a novel unsupervised framework based on an edge-guided in-painting that optimises the learning of contextual attention on only healthy leaf images.

TL;DR: In this paper , the performance of state-of-the-art OOD algorithms on insect detection classifiers is evaluated. But, the authors focus on extrusive algorithms, i.e., algorithms that wrap around a well-trained classifier without the need for additional co-training.

TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.

TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.