Empirically, SWAG performs on par or better than popular alternatives including MC dropout, KFAC Laplace, and temperature scaling on uncertainty quantification, out-of-distribution detection, calibration and transfer learning in computer vision tasks. Code for SWAG is available here.
Reinforcement Learning
In another follow-up paper SWA was shown to improve the performance of policy gradient methods A2C and DDPG on several Atari games and MuJoCo environments.



Environment
A2C
A2C + SWA




Breakout
522 ± 34
703 ± 60


Qbert
18777 ± 778
21272 ± 655


SpaceInvaders
7727 ± 1121
21676 ± 8897


Seaquest
1779 ± 4
1795 ± 4


CrazyClimber
147030 ± 10239
139752 ± 11618


BeamRider
9999 ± 402
11321 ± 1065


