| --- |
| family: hybrid |
| components: [optimization, architecture] |
| introduced-in: "[[liang2023-api]]" |
| stores: [layer input-subspace bases (via DualGPM), per-task expanded channel parameters] |
| requires-boundaries: "yes" |
| requires-task-id: "train+test" |
| memory-growth: sublinear |
| tags: [method] |
| --- |
| |
| # API (Adaptive Plasticity Improvement) |
|
|
| ## What it does |
|
|
| A from-scratch continual learner that couples orthogonal-projection stability |
| with measured, demand-driven expansion. Old tasks are protected by projecting |
| each new-task gradient onto the complement of the accumulated old-task subspace |
| (maintained by DualGPM). Because that projection steadily consumes the space |
| left for new tasks, API measures how much of the raw new-task gradient survives |
| the projection — a per-layer gradient-retention ratio (GRR) it shows correlates |
| with new-task accuracy — and, before training each task, expands only the layers |
| whose retention has fallen below a target, adding input channels in proportion |
| to the deficit. So stability comes from the projection and plasticity is |
| restored exactly where the projection has depleted it. |
|
|
| ## Aspects |
|
|
| - **Stores**: the DualGPM old-task subspace bases (see [[dualgpm]]) plus the |
| per-task expanded channel parameters Bₗ,ₜ. No raw samples, no stored |
| gradients. Expanded parameters (API-Param) grow monotonically but stay small |
| relative to the basis store (Split CIFAR100: total 2.0M, capacity 105%). |
| - **Update rule**: for each new task t, (1) compute per-layer GRR(l,t) = |
| E‖ĝₗ,ₜ‖/‖gₗ,ₜ‖ (surviving projected-gradient norm over raw), (2) set added |
| input dimension dₗ ← dₗ + max(⌊K(ρ − GRR(l,t)) + 0.5⌋, 0) so low-retention |
| layers grow and layers with GRR ≥ ρ do not, (3) train the (now wider) network |
| with DualGPM-projected SGD; the newly added channels' transform Bₗ,ₜ is |
| trained for the current task while the portions of Bₗ,ₜ belonging to previous |
| tasks are frozen. K=10, ρ=0.5 across all experiments. |
| - **Requires**: task boundaries at training; task identity at *both* train and |
| test — inference selects the task-specific expanded weight Wₗ,ₜ by task id |
| (multi-head task-IL). From-scratch; per-layer ε_th matched to GPM; K, ρ tuned |
| for the memory/accuracy trade-off. |
| - **Costs**: memory sublinear in tasks (DualGPM store rises then falls; |
| expanded params grow slowly). Compute: one GRR estimation pass + DualGPM SVDs |
| per task; training projects every mini-batch gradient. Inference is a single |
| forward pass through the task-selected weights. |
| |
| ## Documented behavior |
| |
| - [[liang2023-api]] (introduces): task-IL from-scratch, 20-task streams — best |
| ACC on all four benchmarks (Split CIFAR100 81.4 / BWT −0.8; Split |
| Mini-Imagenet 65.9 / −0.3; 5-Datasets 91.1 / −0.5; CIFAR100-sup 60.2) above |
| GPM (78.9 / 61.2), TRGP (80.5 / 62.5) and expansion baselines, at lower memory |
| than GPM and lower capacity (105%) than DEN/RCL/APD (130–191%). Ablations: the |
| GRR-driven expansion beats a fixed-per-task "Equal" channel budget at matched |
| accuracy; DualGPM-vs-GPM backing changes memory not accuracy; API's gain over |
| GPM widens with task index as GPM's plasticity falls. Authors flag the method |
| is confined to the task-IL setting (task id at test) and leave class-IL / |
| task-free extension open. |
| |
| ## Links |
| |
| combines:: [[dualgpm]] (supplies the old-task gradient subspace and the orthogonal projection that provides stability; API adds the measured expansion on top) |
| uses:: [[orthogonal-update-projection]] (new-task gradients projected onto the old-task subspace complement each step — DualGPM's constraint, providing the stability half) |
| uses:: [[retention-gated-channel-expansion]] (per-layer input channels added before each task in proportion to the gradient-retention deficit ⌊K(ρ − GRR(l,t))+0.5⌋, so only projection-choked layers grow — GRR read directly off the projection as a data-free plasticity gauge) |
| |
| ## Papers engaging this method |
| |
| ```dataview |
| LIST FROM [[]] AND ("papers" OR "influences") |
| ``` |
| |
