{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QXLRADKRSILXPTAXQKP3YMZ2YF","short_pith_number":"pith:QXLRADKR","canonical_record":{"source":{"id":"1703.04332","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-13T11:19:00Z","cross_cats_sorted":[],"title_canon_sha256":"c4b8ce6a5be2a69aa7b73c86a5248f097c90f6ee3b346aba467ac807416447f4","abstract_canon_sha256":"c6e675f1c8459ecb066bda54453fd7cb181a715a4187c826f21b2e1efb10c588"},"schema_version":"1.0"},"canonical_sha256":"85d7100d51921777cc17829fbc333ac166a50ec4fde96318778178efeb3ccf0f","source":{"kind":"arxiv","id":"1703.04332","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.04332","created_at":"2026-05-18T00:27:52Z"},{"alias_kind":"arxiv_version","alias_value":"1703.04332v4","created_at":"2026-05-18T00:27:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.04332","created_at":"2026-05-18T00:27:52Z"},{"alias_kind":"pith_short_12","alias_value":"QXLRADKRSILX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QXLRADKRSILXPTAX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QXLRADKR","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QXLRADKRSILXPTAXQKP3YMZ2YF","target":"record","payload":{"canonical_record":{"source":{"id":"1703.04332","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-13T11:19:00Z","cross_cats_sorted":[],"title_canon_sha256":"c4b8ce6a5be2a69aa7b73c86a5248f097c90f6ee3b346aba467ac807416447f4","abstract_canon_sha256":"c6e675f1c8459ecb066bda54453fd7cb181a715a4187c826f21b2e1efb10c588"},"schema_version":"1.0"},"canonical_sha256":"85d7100d51921777cc17829fbc333ac166a50ec4fde96318778178efeb3ccf0f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:52.739054Z","signature_b64":"qlRqGdimfhGuZRZksirMP/bHarAdrUfkKCDe74nAKC4reOHFAHy5UOjIZzXumGVR2NJoIJV9Pk3F/dmueD3ZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"85d7100d51921777cc17829fbc333ac166a50ec4fde96318778178efeb3ccf0f","last_reissued_at":"2026-05-18T00:27:52.738543Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:52.738543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.04332","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:27:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2h6iiwfFkyWvAjRq0dqWSYVoSGvQcZ0yt1ebHpN/+dt3wR8zBxqQIVnkgjWeT0iDLDnP/ZSf9dJRPKxOaCxfCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T08:06:06.423184Z"},"content_sha256":"d0025405bc8a9a978071cfe63efb245630ffc5773a82f94d8b75d0d9a9afeb94","schema_version":"1.0","event_id":"sha256:d0025405bc8a9a978071cfe63efb245630ffc5773a82f94d8b75d0d9a9afeb94"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QXLRADKRSILXPTAXQKP3YMZ2YF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Pitfall of Unsupervised Pre-Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fribourg, Marcus Liwicki (University Of Fribourg, Mathias Seuret, Michele Alberti, Rolf Ingold, Switzerland)","submitted_at":"2017-03-13T11:19:00Z","abstract_excerpt":"The point of this paper is to question typical assumptions in deep learning and suggest alternatives. A particular contribution is to prove that even if a Stacked Convolutional Auto-Encoder is good at reconstructing pictures, it is not necessarily good at discriminating their classes. When using Auto-Encoders, intuitively one assumes that features which are good for reconstruction will also lead to high classification accuracy. Indeed, it became research practice and is a suggested strategy by introductory books. However, we prove that this is not always the case. We thoroughly investigate the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.04332","kind":"arxiv","version":4},"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":"2026-05-18T00:27:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/zmldwCal71D2PnZV2jgoTbFfN028838bzi4yo3LI1d8ki5IWpaURSKQZancnmNav7bLPCC0U2WfeB072RhzBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T08:06:06.423547Z"},"content_sha256":"575621def4c3a61ae119349706e563e80f770d9fe8f0aeced07975544297fb60","schema_version":"1.0","event_id":"sha256:575621def4c3a61ae119349706e563e80f770d9fe8f0aeced07975544297fb60"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QXLRADKRSILXPTAXQKP3YMZ2YF/bundle.json","state_url":"https://pith.science/pith/QXLRADKRSILXPTAXQKP3YMZ2YF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QXLRADKRSILXPTAXQKP3YMZ2YF/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":"2026-06-02T08:06:06Z","links":{"resolver":"https://pith.science/pith/QXLRADKRSILXPTAXQKP3YMZ2YF","bundle":"https://pith.science/pith/QXLRADKRSILXPTAXQKP3YMZ2YF/bundle.json","state":"https://pith.science/pith/QXLRADKRSILXPTAXQKP3YMZ2YF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QXLRADKRSILXPTAXQKP3YMZ2YF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QXLRADKRSILXPTAXQKP3YMZ2YF","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":"c6e675f1c8459ecb066bda54453fd7cb181a715a4187c826f21b2e1efb10c588","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-13T11:19:00Z","title_canon_sha256":"c4b8ce6a5be2a69aa7b73c86a5248f097c90f6ee3b346aba467ac807416447f4"},"schema_version":"1.0","source":{"id":"1703.04332","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.04332","created_at":"2026-05-18T00:27:52Z"},{"alias_kind":"arxiv_version","alias_value":"1703.04332v4","created_at":"2026-05-18T00:27:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.04332","created_at":"2026-05-18T00:27:52Z"},{"alias_kind":"pith_short_12","alias_value":"QXLRADKRSILX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QXLRADKRSILXPTAX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QXLRADKR","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:575621def4c3a61ae119349706e563e80f770d9fe8f0aeced07975544297fb60","target":"graph","created_at":"2026-05-18T00:27:52Z","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":"The point of this paper is to question typical assumptions in deep learning and suggest alternatives. A particular contribution is to prove that even if a Stacked Convolutional Auto-Encoder is good at reconstructing pictures, it is not necessarily good at discriminating their classes. When using Auto-Encoders, intuitively one assumes that features which are good for reconstruction will also lead to high classification accuracy. Indeed, it became research practice and is a suggested strategy by introductory books. However, we prove that this is not always the case. We thoroughly investigate the","authors_text":"Fribourg, Marcus Liwicki (University Of Fribourg, Mathias Seuret, Michele Alberti, Rolf Ingold, Switzerland)","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-13T11:19:00Z","title":"A Pitfall of Unsupervised Pre-Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.04332","kind":"arxiv","version":4},"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:d0025405bc8a9a978071cfe63efb245630ffc5773a82f94d8b75d0d9a9afeb94","target":"record","created_at":"2026-05-18T00:27:52Z","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":"c6e675f1c8459ecb066bda54453fd7cb181a715a4187c826f21b2e1efb10c588","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-13T11:19:00Z","title_canon_sha256":"c4b8ce6a5be2a69aa7b73c86a5248f097c90f6ee3b346aba467ac807416447f4"},"schema_version":"1.0","source":{"id":"1703.04332","kind":"arxiv","version":4}},"canonical_sha256":"85d7100d51921777cc17829fbc333ac166a50ec4fde96318778178efeb3ccf0f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85d7100d51921777cc17829fbc333ac166a50ec4fde96318778178efeb3ccf0f","first_computed_at":"2026-05-18T00:27:52.738543Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:52.738543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qlRqGdimfhGuZRZksirMP/bHarAdrUfkKCDe74nAKC4reOHFAHy5UOjIZzXumGVR2NJoIJV9Pk3F/dmueD3ZCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:52.739054Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.04332","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d0025405bc8a9a978071cfe63efb245630ffc5773a82f94d8b75d0d9a9afeb94","sha256:575621def4c3a61ae119349706e563e80f770d9fe8f0aeced07975544297fb60"],"state_sha256":"2bcdc4f445b456b0032fef0f9acb207c9e1b8cac71b0953ffdcc536db112b6a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vz4rkdaMJDfC3sn2ECytMhA0JX8korIpY7dHyUx9x9vzNTgHXUtT/q08TWipRD+3os/N6ZcJGOzo4eklIO2iAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T08:06:06.425487Z","bundle_sha256":"3e8a2bee07d26a6f5ae1d4353d4be7debe2e8f2570404f415d7cf0c338609e9b"}}