Robust Optimization for Deep Regression
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection. The network optimization has been usually performed with L2 loss and without considering the impact of outliers on the training process, where an outlier in this context is defined by a sample estimation that lies at an abnormal distance from the other training sample estimations in the objective space. In this work, we propose a regression model with ConvNets that achieves robustness to such outliers by minimizing Tukey's biweight function, an M-estimator robust to outliers, as the loss function for the ConvNet. In addition to the robust loss, we introduce a coarse-to-fine model, which processes input images of progressively higher resolutions for improving the accuracy of the regressed values. In our experiments, we demonstrate faster convergence and better generalization of our robust loss function for the tasks of human pose estimation and age estimation from face images. We also show that the combination of the robust loss function with the coarse-to-fine model produces comparable or better results than current state-of-the-art approaches in four publicly available human pose estimation datasets.
Problem 3. Use Robust optimization to reformulate the following uncertain linear programming prob...
Problem #5 -- Consider the following linear programming problem: Maximize Z = 2x1 + 4x2 + 3x3 subject to: X1 + 3x2 + 2x3 S 30 best to X1 + x2 + x3 S 24 3x1 + 5x2 + 3x3 5 60 and X120, X220, X3 2 0. You are given the information that x > 0, X2 = 0, and x3 >O in the optimal solution. Using the given information and the theory of the simplex method, analyze the...
Styles Problem 15, p. 850 Given this linear programming model, solve the model and then answer the questions t follow Maximize Z = 12x1 + 18x2 + 15x3 where x1 = the quantity of product 1 to make, etc. Subject to Machine 5x1 + 4x2 + 3x3 S 160 minutes Labor 4x1 + 10x2 + 4x3 = 288 hours Materials 2x1 + 2x2 + 4x3 200 pounds Product 2 x2 s 16 units x1, x2, x320 not change 1 If...
2- The Lofton Company has developed the following linear programming problem with the following functional constraints. Max x1 + x2 s.t. 2x1 + x2 ≤ 10 2x1 + 3x2 ≤ 24 3x1 + 4x2 ≥ 36 After running the solver, they found it infeasible so in revision, Lofton drops the original objective and establishes the three goals: Goal 1: Don't exceed 10 in constraint 1. Goal 2: Don't fall short of 36 in constraint 3. Goal 3: Don't exceed 24...
Use the simplex method to solve the linear programming problem. Maximize subject to z=900x4 + 800x2 + 400x3 X1 + x2 + x3 = 110 2X1 + 3x2 + 4x3 = 340 2xy + x2 + x3 180 X1 20, X220, X3 20. Select the correct choice below and, if necessary, fill in the answer boxes to complete your choice. O A. The maximum is when x1 = ,x2 = , x3 = ,s2 = ,s2 =), and s3 =...
Consider the following linear programming problem Manimize $45X1 + $10X2 Subject To 15X1 + 5X2 2 1000 Constraint A 20X1 + 4X2 > 1200 Constraint B X1, X2 20 Constraint C if A and B are the two binding constraints. a) What is the range of optimality of the objective function? 3 C1/C2 s 5 b) Suppose that the unit revenues for X1 and X2 are changed to $100 and $15, respectively. Will the current optimum remain the same? NO...
1. Solving the linear programming problem Maximize z 3r1 2r2 3, subject to the constraints using the simplex algorithm gave the final tableau T4 T5 #210 1-1/4 3/8-1/812 0 0 23/4 3/8 7/8 10 (a) (3 points) Add the constraint -221 to the final tableau and use the dual simplex algorithm to find a new optimal solution. (b) (3 points) After adding the constraint of Part (a), what happens to the optimal solution if we add the fourth constraint 2+...
Use this output to answer these questions please, I need to understand. Interpreting an LP output after solving the problem using the software. The following linear programming problem has been solved using the software. Use the output to answer the questions below LINEAR PROGRAMMING PROBLEM MAX 25x1+30x2+15x3 ST. 1) 4X1+5X2+8X3<1200 2) 9x1+15X2+3X3c1500 OPTIMAL SOLUTION: Objective Function Value- 4700.000 Variable Value 140.000 duced Costs 0.000 10.000 0.000 x1 x2 X3 0.000 80.000 Slack/Surplus 0.000 0.000 1.000 2.333 2 OBJECTIVE COEFFICIENT RANGES:...