Auto Seed Vl2 ((better)) Info

: (1) Performance on highly structured tasks (e.g., VQA with relational reasoning) drops by 6% compared to exemplar replay. (2) The generator’s meta-update requires 5% of training data as a validation set – not always available. (3) Seed interpretability: unlike real images, seeds are opaque vectors.

We then generate ( m ) new seeds specific to task ( t ) using ( G_\phi(z_t) ). These are added to the seed memory ( \mathcalS ). To prevent unbounded memory growth, we apply : remove seeds whose mutual information with the task label is below a threshold ( \tau_\textmi = 0.3 ) (estimated via kernel density estimation). auto seed vl2

We ask a critical question: Can we generate synthetic, compact representations of past tasks on the fly, without storing any real examples? : (1) Performance on highly structured tasks (e

This paper answers affirmatively with , a framework where a meta-learned generator produces "seed vectors"—low-dimensional embeddings that encapsulate a task’s visual-textual distribution. During training on a new task, the model replays these seeds alongside current data, then updates the generator to refine seeds for future recall. The key contributions are: We then generate ( m ) new seeds

: The tool interacts with the game's menu interface to bypass manual input.