{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/hope","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/907face6-0035-445a-833a-0b1e2045322a","json_url":"https://onlylabs.fyi/signals/907face6-0035-445a-833a-0b1e2045322a/signal.json","generated_at":"2026-10-29T15:59:43.748Z","evidence_latest_fetched_at":"2026-10-28T07:04:34.814833+00:00","signal_first_seen_at":"2026-10-28T07:00:33.111884+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/907face6-0035-445a-833a-0b1e2045322a","signal_json":"https://onlylabs.fyi/signals/907face6-0035-445a-833a-0b1e2045322a/signal.json","source":"https://github.com/google-deepmind/hope","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Google (DeepMind / Gemini) published google-deepmind/hope (Python). 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Language: Python License: Apache-2.0 Stars: 3 Forks: 0 Open issues: 0 Created: 2026-10-27T19:55:42Z Pushed: 2026-10-27T21:25:09Z Default branch: main Fork: no Archived: no README: HOPE: Hilbert Operator for Progressive Encoding & DEFT Transfer Learning This repository contains the reference implementations of **HOPE** (*Hilbert Operator for Progressive Encoding*) and **DEFT** (*Dispersed Elastic Fine-Tuning*): 1. **Data-Free Progressive Model Compression (`hope.py`, `compress_resnet50.py`, `hope_baselines.py`, `plot_compression_curves.py`):** HOPE compresses an ImageNet-pretrained ResNet-50 (Keras) through a sequence of analytical steps (pruning a neuron via `PRUNE`, merging two neurons into one synthesized parent neuron via `MERGE`, or evicting the residual branch of a bottleneck block via `EVICT`), selected greedily by a rate-distortion criterion. Every step is recorded together with the information needed to undo it. 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