Instance Conditioned Adaptation

Instance-conditioned adaptation focuses on tailoring machine learning models to specific input instances, improving performance beyond what's achievable with general-purpose models. Current research explores this through various methods, including adaptive parameter adjustments (e.g., using low-rank updates or learnable modules), instance-specific feature selection, and the development of instance-aware training schemes like reinforcement learning. This approach addresses limitations in handling data heterogeneity and long-tailed distributions, leading to improved generalization and efficiency in diverse applications such as image compression, combinatorial optimization, and federated learning.