English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A large obstacle to deploying deep learning models in practice is the processof updating models post-deployment (ideally, frequently). Deep neural networkscan cost many thousands of dollars to train. When new data comes in thepipeline, you can train a new model from scratch (randomly initialized weights)on all existing data. Instead, you can take an existing model and fine-tune(continue to train) it on new data. The former is costly and slow. The latteris cheap and fast, but catastrophic forgetting generally causes the new modelto 'forget' how to classify older data well. There are a plethora ofcomplicated techniques to keep models from forgetting their past learnings.Arguably the most basic is to mix in a small amount of past data into the newdata during fine-tuning: also known as 'data rehearsal'. In this paper, wecompare various methods of limiting catastrophic forgetting and conclude thatif you can maintain access to a portion of your past data (or tasks), datarehearsal is ideal in terms of overall accuracy across all time periods, andperforms even better when combined with methods like Elastic WeightConsolidation (EWC). Especially when the amount of past data (past 'tasks') islarge compared to new data, the cost of updating an existing model is farcheaper and faster than training a new model from scratch.

Catastrophic Forgetting in the Context of Model Updates