46How do you choose a loss function (MSE, MAE, Huber, cross-entropy, focal, contrastive)?▼medium★ EssentialGoogleMetaAmazon1 replies◆ premiumThe loss defines what the model optimizes, and picking the wrong one quietly dooms it. The signal is matching the loss to the task and data (outliers, imbalance), not defaulting to MSE or cross-entropy on reflex.Open full answer →
95How do you choose loss functions for computer vision tasks (classification, detection, segmentation)?▼mediumGoogleMetaNVIDIA1 replies◆ premiumCross-entropy is the start, not the answer. The signal is matching the loss to the task structure: focal for detection's background flood, IoU-family for box overlap, Dice for imbalanced masks. Here is how to reason about it.Open full answer →
109Compare SMOTE, class reweighting, and focal loss for imbalanced learning. Which do you reach for?▼hardAmazonGoogleMicrosoft2 replies◆ premiumResampling, reweighting, and focal loss attack class imbalance from different angles, and each has a real downside. The signal is matching the method to the model and metric, not blindly oversampling. Here is the breakdown.Open full answer →
