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📚TheoryIntermediate

Multi-Task Loss Balancing

Multi-task loss balancing aims to automatically set each task’s weight so that no single loss dominates training.

#multi-task learning#uncertainty weighting#homoscedastic uncertainty+12
∑MathIntermediate

Surrogate Loss Theory

0-1 loss directly measures classification error but is discontinuous and non-convex, making optimization computationally hard.

#surrogate loss
Advanced
Group:
Loss Functions & Objectives
#0-1 loss
#hinge loss
+12
📚TheoryIntermediate

Knowledge Distillation Loss

Knowledge distillation loss blends standard hard-label cross-entropy with a soft distribution match from a teacher using a temperature parameter.

#knowledge distillation#kd loss#temperature scaling+12
📚TheoryAdvanced

CTC Loss (Connectionist Temporal Classification)

CTC loss trains sequence models when you do not know the alignment between inputs (frames) and outputs (labels).

#ctc loss#connectionist temporal classification#forward backward+12
📚TheoryIntermediate

Perceptual Loss & Feature Matching

Perceptual loss compares images in a deep network's feature space rather than raw pixels, which aligns better with human judgment of similarity.

#perceptual loss#feature matching#gan+12
📚TheoryIntermediate

Triplet Loss & Contrastive Loss

Triplet loss and contrastive loss are metric-learning objectives that teach a model to map similar items close together and dissimilar items far apart in an embedding space.

#triplet loss#contrastive loss#metric learning+12
📚TheoryIntermediate

Focal Loss

Focal Loss reshapes cross-entropy so that hard, misclassified examples get more focus while easy, well-classified ones are down-weighted.

#focal loss#class imbalance#cross-entropy+11
∑MathIntermediate

Huber Loss & Smooth L1

Huber loss behaves like mean squared error (quadratic) for small residuals and like mean absolute error (linear) for large residuals, making it both stable and robust.

#huber loss#smooth l1#robust regression+12
∑MathBeginner

Mean Squared Error (MSE)

Mean Squared Error (MSE) measures the average of the squared differences between true values and predictions, punishing larger mistakes more strongly.

#mean squared error#mse#sse+11
∑MathIntermediate

Cross-Entropy Loss

Cross-entropy loss measures how well predicted probabilities match the true labels by penalizing confident wrong predictions heavily.

#cross-entropy#binary cross-entropy#softmax+11