FLAME: Feedforward Latent Task Inference for Continual Meta-Learning

Continual‑meta learning promises rapid adaptation to new tasks without forgetting old ones. Despite significant progress, two major challenges remain. First, existing works mainly considered MAML as the meta-learner where adaptation is limited to gradient descent and meta-knowledge is encoded in initial model weights, creating bottlenecks as the task landscape becomes complex. Second, existing works largely operate in task-aware settings that rely on known task boundaries and identities to pair context/query samples, while the few task-agnostic variants forgo the bi-level meta-objective. We address these two challenges with FLAME, a $\text{feedforward meta-model}$ with $\text{latent task inference}$ for continual adaptation. FLAME uses a feedforward meta-learner to rapidly adapt either the classifier layer or, in a nested formulation, the feature extractor of a DNN. It then leverages the feedforward task embedding to detect task boundaries and estimate task identities, approximating bi-level meta-optimization in a $\text{task-agnostic}$ fashion. The continually-consolidated task relations in the replay reservoir further enables recognition of known $vs.$ novel tasks, allow continual improvements on recurring tasks over various lengths of local stationary windows. We demonstrated the improved performance of FLAME in comparison to both task-agnostic and task-aware baselines, in domain- and class-incremental learning across image and text datasets.