Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation
Bibliographic record
Abstract
Deep neural networks are susceptible to catastrophic forgetting: when encountering a new task, they can only remember the new task and fail to preserve its ability to accomplish previously learned tasks. In this paper, we study the problem of lifelong learning for generative models and propose a novel and generic continual learning framework Hyper-LifelongGAN which is more scalable compared with state-of-the-art approaches. Given a sequence of tasks, the conventional convolutional filters are factorized into the dynamic base filters which are generated using task specific filter generators, and deterministic weight matrix which linearly combines the base filters and is shared across different tasks. Moreover, the shared weight matrix is multiplied by task specific coefficients to introduce more flexibility in combining task specific base filters differently for different tasks. Attributed to the novel architecture, the proposed method can preserve or even improve the generation quality at a low cost of parameters. We validate Hyper-LifelongGAN on diverse image-conditioned generation tasks, extensive ablation studies and comparisons with state-of-the-art models are carried out to show that the proposed approach can address catastrophic forgetting effectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".