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1、Training Region-based Object Detectors with Online Hard Example MiningAbhinav Shrivastava, Abhinav Gupta, Ross Girshick Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollar MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels Lu Ji

2、ang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, Li Fei-Fei Reporter: Fujin HeTraining Region-based Object Detectors with Online Hard Example MiningTraining Region-based Object Detectors with Online Hard Example MiningPART 01a large imbalance between the number of annotated objects and the number of ba

3、ckground examples , such as the deformable parts model (DPM) , this imbalance may beas extreme as 100,000 background examples to every oneobject. The recent trend towards object-proposal-based detectors mitigates this issue to an extent, but the imbalance ratio may still be high (e.g., 70:1) challen

4、geFor some period of time a fixed model is used to find new examples to add to the active training set.For some period of time the model is trained on the fixed active training set. ONEBenefitsIt removes the need for several heuristics and hyperparameters commonly used in region-based ConvNets .TWOI

5、t yields a consistent and significant boosts in mean average precision. THREEIts effectiveness increases as the training set es larger and more difficult, as emonstrated by results on the MS COCO dataset. Focal Loss for Dense Object Detection MentorNet: Regularizing Very Deep Neural Networks on Corr

6、upted Labels MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels PART 03TWOTHREEONEWe discover that deep CNNs trained on corr-upted labels can be improved by learning another network toweigh training examples. We propose an algorithm to optimize MentorNet with deep CNNs on big data

7、 and prove its conv-ergence under standard and mild assumptions. We empirically verify the proposed model on 4 datasets of both controlled and real-world noisy labels.ModelObjective FunctionD is training set, yi is the corresponding noisy label vector. Let gs denote the dis-Criminative function of a

8、 neural network called StuentendNet, L is a m-dimensional cloumn vector, denote the loss over m classes. v is a vector to represent the weight for i-th example, the function G is the explicit data regularizer(a technique adding a penalty term to the error function to prevent overfitting) ang lamda i

9、s the hyperparmeter, G determines the complexity of a weighting scheme, and imposes an (unnormalized) weight distribution over all training examples. AlgorithmIn the subroutine of minimizing w when fixing v, stochastic gradient descent often takes many steps before converging. This means that it can take a long time before moving past this single sub-step.More importantly, the subroutine of minimizing v