{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:J2YGK5MHEICCWOFKMVNIPHXNSP","short_pith_number":"pith:J2YGK5MH","canonical_record":{"source":{"id":"2007.06712","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-19T15:49:39Z","cross_cats_sorted":[],"title_canon_sha256":"f66fe815e56bf1d1019715000cc85dcc2308220245e66b3f5fefb3dcf503e391","abstract_canon_sha256":"0459f8157ca18a7cc0da874440d95b134beb959b29732f9f50bba68510d84074"},"schema_version":"1.0"},"canonical_sha256":"4eb065758722042b38aa655a879eed93e36cdeb286f8b07f3064c3a13d3f300e","source":{"kind":"arxiv","id":"2007.06712","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.06712","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"arxiv_version","alias_value":"2007.06712v1","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.06712","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"pith_short_12","alias_value":"J2YGK5MHEICC","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"pith_short_16","alias_value":"J2YGK5MHEICCWOFK","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"pith_short_8","alias_value":"J2YGK5MH","created_at":"2026-07-05T01:18:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:J2YGK5MHEICCWOFKMVNIPHXNSP","target":"record","payload":{"canonical_record":{"source":{"id":"2007.06712","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-19T15:49:39Z","cross_cats_sorted":[],"title_canon_sha256":"f66fe815e56bf1d1019715000cc85dcc2308220245e66b3f5fefb3dcf503e391","abstract_canon_sha256":"0459f8157ca18a7cc0da874440d95b134beb959b29732f9f50bba68510d84074"},"schema_version":"1.0"},"canonical_sha256":"4eb065758722042b38aa655a879eed93e36cdeb286f8b07f3064c3a13d3f300e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:18:22.527326Z","signature_b64":"Uz3/AmP9JFNQo1FPvNY6qU2LnIaFEMbTVa6vZn+M/MRYOulwc4RjQTkjyQtYLLbe7jqsC2imFtLyM8uTArTFCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4eb065758722042b38aa655a879eed93e36cdeb286f8b07f3064c3a13d3f300e","last_reissued_at":"2026-07-05T01:18:22.526843Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:18:22.526843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.06712","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":"2026-07-05T01:18:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kqojbaWeF3EWhHM7klkhFUcfpvrp4nhNATJr4kafcORDJuQ42fjx44LNnw4FGkHw4Ie5rhm3adpTceX/52veCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:13:29.316328Z"},"content_sha256":"f6b4fca77b0a16c834b21a768772779eb2abab65767dccd07bc5abebfd8c14c3","schema_version":"1.0","event_id":"sha256:f6b4fca77b0a16c834b21a768772779eb2abab65767dccd07bc5abebfd8c14c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:J2YGK5MHEICCWOFKMVNIPHXNSP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amirhossein Tavanaei","submitted_at":"2020-06-19T15:49:39Z","abstract_excerpt":"Understanding intermediate layers of a deep learning model and discovering the driving features of stimuli have attracted much interest, recently. Explainable artificial intelligence (XAI) provides a new way to open an AI black box and makes a transparent and interpretable decision. This paper proposes a new explainable convolutional neural network (XCNN) which represents important and driving visual features of stimuli in an end-to-end model architecture. This network employs encoder-decoder neural networks in a CNN architecture to represent regions of interest in an image based on its catego"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.06712","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2007.06712/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:18:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ecD2DkOZVk5BKKsbsePG/9FF75mVaYKOjEEWWPDqiRXjC3d8I7oOQPYkdb6eLZCugtaOeZtjnUiof+mCKe3UAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:13:29.318807Z"},"content_sha256":"755c7319c82d360b54f4074f07cfbdf2eadcbacd71199e30ac9083771f369e0a","schema_version":"1.0","event_id":"sha256:755c7319c82d360b54f4074f07cfbdf2eadcbacd71199e30ac9083771f369e0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J2YGK5MHEICCWOFKMVNIPHXNSP/bundle.json","state_url":"https://pith.science/pith/J2YGK5MHEICCWOFKMVNIPHXNSP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J2YGK5MHEICCWOFKMVNIPHXNSP/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":"2026-08-09T11:13:29Z","links":{"resolver":"https://pith.science/pith/J2YGK5MHEICCWOFKMVNIPHXNSP","bundle":"https://pith.science/pith/J2YGK5MHEICCWOFKMVNIPHXNSP/bundle.json","state":"https://pith.science/pith/J2YGK5MHEICCWOFKMVNIPHXNSP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J2YGK5MHEICCWOFKMVNIPHXNSP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:J2YGK5MHEICCWOFKMVNIPHXNSP","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":"0459f8157ca18a7cc0da874440d95b134beb959b29732f9f50bba68510d84074","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-19T15:49:39Z","title_canon_sha256":"f66fe815e56bf1d1019715000cc85dcc2308220245e66b3f5fefb3dcf503e391"},"schema_version":"1.0","source":{"id":"2007.06712","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.06712","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"arxiv_version","alias_value":"2007.06712v1","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.06712","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"pith_short_12","alias_value":"J2YGK5MHEICC","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"pith_short_16","alias_value":"J2YGK5MHEICCWOFK","created_at":"2026-07-05T01:18:22Z"},{"alias_kind":"pith_short_8","alias_value":"J2YGK5MH","created_at":"2026-07-05T01:18:22Z"}],"graph_snapshots":[{"event_id":"sha256:755c7319c82d360b54f4074f07cfbdf2eadcbacd71199e30ac9083771f369e0a","target":"graph","created_at":"2026-07-05T01:18:22Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2007.06712/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Understanding intermediate layers of a deep learning model and discovering the driving features of stimuli have attracted much interest, recently. Explainable artificial intelligence (XAI) provides a new way to open an AI black box and makes a transparent and interpretable decision. This paper proposes a new explainable convolutional neural network (XCNN) which represents important and driving visual features of stimuli in an end-to-end model architecture. This network employs encoder-decoder neural networks in a CNN architecture to represent regions of interest in an image based on its catego","authors_text":"Amirhossein Tavanaei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-19T15:49:39Z","title":"Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.06712","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:f6b4fca77b0a16c834b21a768772779eb2abab65767dccd07bc5abebfd8c14c3","target":"record","created_at":"2026-07-05T01:18:22Z","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":"0459f8157ca18a7cc0da874440d95b134beb959b29732f9f50bba68510d84074","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-06-19T15:49:39Z","title_canon_sha256":"f66fe815e56bf1d1019715000cc85dcc2308220245e66b3f5fefb3dcf503e391"},"schema_version":"1.0","source":{"id":"2007.06712","kind":"arxiv","version":1}},"canonical_sha256":"4eb065758722042b38aa655a879eed93e36cdeb286f8b07f3064c3a13d3f300e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4eb065758722042b38aa655a879eed93e36cdeb286f8b07f3064c3a13d3f300e","first_computed_at":"2026-07-05T01:18:22.526843Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:18:22.526843Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Uz3/AmP9JFNQo1FPvNY6qU2LnIaFEMbTVa6vZn+M/MRYOulwc4RjQTkjyQtYLLbe7jqsC2imFtLyM8uTArTFCA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:18:22.527326Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.06712","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6b4fca77b0a16c834b21a768772779eb2abab65767dccd07bc5abebfd8c14c3","sha256:755c7319c82d360b54f4074f07cfbdf2eadcbacd71199e30ac9083771f369e0a"],"state_sha256":"dbd66fac54d296c8e2d2c693645c911a3ac293bc8d5e2de1365a2dc6b86a30e2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xqGdTEBNtIHVaJf7okmuKa+/HjR5Uv0RG82se/dUxwXcuP2xrzqAjmaf8v9lcbBjVCKLKSzWJ+4065M9vcmFAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T11:13:29.323847Z","bundle_sha256":"6af421a200032315780f70e9ed42c09a03f66a74aea64efe5ad018cf23b33e60"}}