Training data ambiguity, such as the presence of out-of-frame moving objects, introduces significant challenges in deep learning-based optical flow models by causing large loss spikes and training instability. Most models overlook this ambiguity, treating it as a limitation of existing datasets. SEA-RAFT attempts to address these ambiguous areas with an additional network and a modified loss function, yet does so without explicitly verifying the specific drawbacks. In this paper, we investigate the influence of out-of-frame movements on model accuracy by generating the FlyingIcons dataset, which includes in-frame and out-of-frame masks for precise analysis. Using the latter masks, we introduce a weighted masked training scheme that selectively penalizes errors in out-of-frame areas, significantly increasing model accuracy over both standard GMA training and SEA-RAFT. Building on this concept, we propose a weighted partially masked training method, which uses partial out-of-frame masks generated through a simple process that adds the ground truth flow to pixel locations and checks if they fall outside the frame. While this method only yields improvements in error reduction on FlyingThings3D, our findings suggest that incorporating similar masks into other synthetic datasets could improve model stability and accuracy with minimal additional overhead. This highlights a promising direction for further research, particularly in developing more complex mask generation strategies and creating synthetic datasets with out-of-frame masks to enhance generalizability across datasets.

CycleGANs [1] and CIConv [2] are both relatively new approaches to their respective applications. For CycleGANs this application is unpaired image-to-image domain adaptation and for CIConv this application is making images more
robust to illumination changes. We investigate whether CycleGANs in combination with CIConv can be used to improve the day-night domain adaptation. The resulting images could then be used during the training of CNNs that can be found in self-driving cars. Attempts were made to get the CycleGANs in combination with CIConv to train in a stable manner. These attempts included a variety of hyperparameter combinations, a number of architecture alterations and training procedure adjustments, and most significantly two different loss functions. Both these loss functions apply a CycleConsistency Loss, one applies an additional Adversarial Loss [1] and the other an additional Wasserstein Distance and Gradient Penalty [3]. In this paper we show that CycleGANs with CIConv as the first layer in either the Discriminators or the Generators resulted in unstable training. We conclude that the root of the instability issues lies in the CIConv layer causing exploding gradients resulting in unsuccessful training of the model. Finally, we propose an adjustment to the CIConv layer which shows promise in resolving these issues for the architecture with CIConv in the Generators. However, no extensive testing has been done.