{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:33VALGJLVRZVWFVKMJDUXBT7DY","short_pith_number":"pith:33VALGJL","canonical_record":{"source":{"id":"1808.05577","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-16T16:42:10Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.NC"],"title_canon_sha256":"745759dade1f6a5149ec22fbdf16c91475752621eac8902a40c3153ddc0a6125","abstract_canon_sha256":"3414136ebc8d10ab0ba433b03c165ea163f7733460f812cfe6511bc7c522c957"},"schema_version":"1.0"},"canonical_sha256":"deea05992bac735b16aa62474b867f1e20196a2edac241569cb5485994466994","source":{"kind":"arxiv","id":"1808.05577","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.05577","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"1808.05577v1","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05577","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"33VALGJLVRZV","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"33VALGJLVRZVWFVK","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"33VALGJL","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:33VALGJLVRZVWFVKMJDUXBT7DY","target":"record","payload":{"canonical_record":{"source":{"id":"1808.05577","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-16T16:42:10Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.NC"],"title_canon_sha256":"745759dade1f6a5149ec22fbdf16c91475752621eac8902a40c3153ddc0a6125","abstract_canon_sha256":"3414136ebc8d10ab0ba433b03c165ea163f7733460f812cfe6511bc7c522c957"},"schema_version":"1.0"},"canonical_sha256":"deea05992bac735b16aa62474b867f1e20196a2edac241569cb5485994466994","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:56.578212Z","signature_b64":"3nbjlE+k9z1EZrS09h8zbLA1kTeWyn2PsAtxvgVFz3ZPF1Wq/OtmYdJh5ZM1xP/dRf2Jq4FcjBGS2tdC+dbXAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"deea05992bac735b16aa62474b867f1e20196a2edac241569cb5485994466994","last_reissued_at":"2026-05-18T00:07:56.577567Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:56.577567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.05577","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BtB2Cv/G37pSjuqFD29QxN/LoSPh8V+l8URDycPjYu1A1UjrXFWc4kYZzTM30ppUKoCu/Mz770JfV4XpnHsEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T21:51:05.569930Z"},"content_sha256":"fda39b512500fb60b075627f548682995444ead0be0f53aeff44f756e60c4bdf","schema_version":"1.0","event_id":"sha256:fda39b512500fb60b075627f548682995444ead0be0f53aeff44f756e60c4bdf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:33VALGJLVRZVWFVKMJDUXBT7DY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-bio.NC"],"primary_cat":"cs.CV","authors_text":"Daniel C. Alexander, Iasonas Kokkinos, Ryutaro Tanno, Stefano B. Blumberg","submitted_at":"2018-08-16T16:42:10Z","abstract_excerpt":"In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $ - $ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05577","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:07:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"InKoQvFE0GnFfmosS4CgNqowU/iMZfSW1odwSIws9zWNmm1ooMePKfihrg3R1BCPRO20uob5wNPJ/dksEBDTCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T21:51:05.570383Z"},"content_sha256":"429b78898d60c3d8d9562efd2877abea5ab95d803c430e75c7217fb6e84a6bca","schema_version":"1.0","event_id":"sha256:429b78898d60c3d8d9562efd2877abea5ab95d803c430e75c7217fb6e84a6bca"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/33VALGJLVRZVWFVKMJDUXBT7DY/bundle.json","state_url":"https://pith.science/pith/33VALGJLVRZVWFVKMJDUXBT7DY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/33VALGJLVRZVWFVKMJDUXBT7DY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T21:51:05Z","links":{"resolver":"https://pith.science/pith/33VALGJLVRZVWFVKMJDUXBT7DY","bundle":"https://pith.science/pith/33VALGJLVRZVWFVKMJDUXBT7DY/bundle.json","state":"https://pith.science/pith/33VALGJLVRZVWFVKMJDUXBT7DY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/33VALGJLVRZVWFVKMJDUXBT7DY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:33VALGJLVRZVWFVKMJDUXBT7DY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3414136ebc8d10ab0ba433b03c165ea163f7733460f812cfe6511bc7c522c957","cross_cats_sorted":["cs.AI","cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-16T16:42:10Z","title_canon_sha256":"745759dade1f6a5149ec22fbdf16c91475752621eac8902a40c3153ddc0a6125"},"schema_version":"1.0","source":{"id":"1808.05577","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.05577","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"arxiv_version","alias_value":"1808.05577v1","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.05577","created_at":"2026-05-18T00:07:56Z"},{"alias_kind":"pith_short_12","alias_value":"33VALGJLVRZV","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"33VALGJLVRZVWFVK","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"33VALGJL","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:429b78898d60c3d8d9562efd2877abea5ab95d803c430e75c7217fb6e84a6bca","target":"graph","created_at":"2026-05-18T00:07:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $ - $ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth o","authors_text":"Daniel C. Alexander, Iasonas Kokkinos, Ryutaro Tanno, Stefano B. Blumberg","cross_cats":["cs.AI","cs.LG","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-16T16:42:10Z","title":"Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.05577","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fda39b512500fb60b075627f548682995444ead0be0f53aeff44f756e60c4bdf","target":"record","created_at":"2026-05-18T00:07:56Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3414136ebc8d10ab0ba433b03c165ea163f7733460f812cfe6511bc7c522c957","cross_cats_sorted":["cs.AI","cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-16T16:42:10Z","title_canon_sha256":"745759dade1f6a5149ec22fbdf16c91475752621eac8902a40c3153ddc0a6125"},"schema_version":"1.0","source":{"id":"1808.05577","kind":"arxiv","version":1}},"canonical_sha256":"deea05992bac735b16aa62474b867f1e20196a2edac241569cb5485994466994","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"deea05992bac735b16aa62474b867f1e20196a2edac241569cb5485994466994","first_computed_at":"2026-05-18T00:07:56.577567Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:07:56.577567Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3nbjlE+k9z1EZrS09h8zbLA1kTeWyn2PsAtxvgVFz3ZPF1Wq/OtmYdJh5ZM1xP/dRf2Jq4FcjBGS2tdC+dbXAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:07:56.578212Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.05577","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fda39b512500fb60b075627f548682995444ead0be0f53aeff44f756e60c4bdf","sha256:429b78898d60c3d8d9562efd2877abea5ab95d803c430e75c7217fb6e84a6bca"],"state_sha256":"de21fcc8b3d7b32e388767488cc1ae7113d2c4782f413783508b920a94fa46a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GuxugKtZFKr/Bt21iSohJaze6OAY7Z2s8DUZsHBmvWX99m+ZyFoMKSOUqVlfQjzeJbz6UQfHq6yvbYZfYrggAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T21:51:05.572358Z","bundle_sha256":"8ad3b49271765a324dc8fe8c32ad62e1c80b98d41e257b6b945d29897e0d5d54"}}