{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:JWXEWZOJK6U437Y3BS4RLNKFKV","short_pith_number":"pith:JWXEWZOJ","canonical_record":{"source":{"id":"1704.06327","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-20T20:29:46Z","cross_cats_sorted":[],"title_canon_sha256":"352d18fd4b417f211ec569e744308ec4c4dc530c263f444ced3c48a191cc2740","abstract_canon_sha256":"82612337df7d953f135ce88d5bcd43f1ffac9dbef7c3ef367e3a569d6d6809ec"},"schema_version":"1.0"},"canonical_sha256":"4dae4b65c957a9cdff1b0cb915b5455577e024f0b7217f6811b53dce10d86ad8","source":{"kind":"arxiv","id":"1704.06327","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.06327","created_at":"2026-05-18T00:38:20Z"},{"alias_kind":"arxiv_version","alias_value":"1704.06327v3","created_at":"2026-05-18T00:38:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06327","created_at":"2026-05-18T00:38:20Z"},{"alias_kind":"pith_short_12","alias_value":"JWXEWZOJK6U4","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JWXEWZOJK6U437Y3","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JWXEWZOJ","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:JWXEWZOJK6U437Y3BS4RLNKFKV","target":"record","payload":{"canonical_record":{"source":{"id":"1704.06327","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-20T20:29:46Z","cross_cats_sorted":[],"title_canon_sha256":"352d18fd4b417f211ec569e744308ec4c4dc530c263f444ced3c48a191cc2740","abstract_canon_sha256":"82612337df7d953f135ce88d5bcd43f1ffac9dbef7c3ef367e3a569d6d6809ec"},"schema_version":"1.0"},"canonical_sha256":"4dae4b65c957a9cdff1b0cb915b5455577e024f0b7217f6811b53dce10d86ad8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:20.728115Z","signature_b64":"OcHJhzGE7a7hPxKYayT0pEzzu+GobcIGDewGKszsOAvBRjMLQdTMSclGLH6io0UE2JVs6BEbxSPTdV5tAGezBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4dae4b65c957a9cdff1b0cb915b5455577e024f0b7217f6811b53dce10d86ad8","last_reissued_at":"2026-05-18T00:38:20.727572Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:20.727572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.06327","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:38:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d47wRZ9tNENcFjKtIgR2o+xTBgs/Uf+GjuWFzP1rJRTh9dvY8Yg2hR1D20j79xCqcVnYOXeAADA9AXW+AMjRCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T23:37:13.165364Z"},"content_sha256":"c17fb1a35838d36343bf9212f99bd41206c52693aadd6e56112395776e2ffad1","schema_version":"1.0","event_id":"sha256:c17fb1a35838d36343bf9212f99bd41206c52693aadd6e56112395776e2ffad1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:JWXEWZOJK6U437Y3BS4RLNKFKV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Amirhossein Herandi, Cheng Deng, Heng Huang, Kamran Ghasedi Dizaji, Weidong Cai","submitted_at":"2017-04-20T20:29:46Z","abstract_excerpt":"Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from lack of efficiency and scalability when dealing with large-scale and high-dimensional data. In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06327","kind":"arxiv","version":3},"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":"2026-05-18T00:38:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"64rPAOL44TzJDL7nN/qXJWmf4XT68SfEs/eih0y23P12zKHb1LBP85hwFs71AmSnwSARTemqwAZyAPCnbP2yCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T23:37:13.165712Z"},"content_sha256":"72bdcb1ff52a4791c11e6775aa6bcd068e9cde0a65333c4a630835b84fe666d8","schema_version":"1.0","event_id":"sha256:72bdcb1ff52a4791c11e6775aa6bcd068e9cde0a65333c4a630835b84fe666d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JWXEWZOJK6U437Y3BS4RLNKFKV/bundle.json","state_url":"https://pith.science/pith/JWXEWZOJK6U437Y3BS4RLNKFKV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JWXEWZOJK6U437Y3BS4RLNKFKV/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":"2026-06-02T23:37:13Z","links":{"resolver":"https://pith.science/pith/JWXEWZOJK6U437Y3BS4RLNKFKV","bundle":"https://pith.science/pith/JWXEWZOJK6U437Y3BS4RLNKFKV/bundle.json","state":"https://pith.science/pith/JWXEWZOJK6U437Y3BS4RLNKFKV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JWXEWZOJK6U437Y3BS4RLNKFKV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:JWXEWZOJK6U437Y3BS4RLNKFKV","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":"82612337df7d953f135ce88d5bcd43f1ffac9dbef7c3ef367e3a569d6d6809ec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-20T20:29:46Z","title_canon_sha256":"352d18fd4b417f211ec569e744308ec4c4dc530c263f444ced3c48a191cc2740"},"schema_version":"1.0","source":{"id":"1704.06327","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.06327","created_at":"2026-05-18T00:38:20Z"},{"alias_kind":"arxiv_version","alias_value":"1704.06327v3","created_at":"2026-05-18T00:38:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06327","created_at":"2026-05-18T00:38:20Z"},{"alias_kind":"pith_short_12","alias_value":"JWXEWZOJK6U4","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"JWXEWZOJK6U437Y3","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"JWXEWZOJ","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:72bdcb1ff52a4791c11e6775aa6bcd068e9cde0a65333c4a630835b84fe666d8","target":"graph","created_at":"2026-05-18T00:38:20Z","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":"Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from lack of efficiency and scalability when dealing with large-scale and high-dimensional data. In this paper, we propose a new clustering model, called DEeP Embedded RegularIzed ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convol","authors_text":"Amirhossein Herandi, Cheng Deng, Heng Huang, Kamran Ghasedi Dizaji, Weidong Cai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-20T20:29:46Z","title":"Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06327","kind":"arxiv","version":3},"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:c17fb1a35838d36343bf9212f99bd41206c52693aadd6e56112395776e2ffad1","target":"record","created_at":"2026-05-18T00:38:20Z","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":"82612337df7d953f135ce88d5bcd43f1ffac9dbef7c3ef367e3a569d6d6809ec","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-04-20T20:29:46Z","title_canon_sha256":"352d18fd4b417f211ec569e744308ec4c4dc530c263f444ced3c48a191cc2740"},"schema_version":"1.0","source":{"id":"1704.06327","kind":"arxiv","version":3}},"canonical_sha256":"4dae4b65c957a9cdff1b0cb915b5455577e024f0b7217f6811b53dce10d86ad8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4dae4b65c957a9cdff1b0cb915b5455577e024f0b7217f6811b53dce10d86ad8","first_computed_at":"2026-05-18T00:38:20.727572Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:38:20.727572Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OcHJhzGE7a7hPxKYayT0pEzzu+GobcIGDewGKszsOAvBRjMLQdTMSclGLH6io0UE2JVs6BEbxSPTdV5tAGezBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:38:20.728115Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.06327","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c17fb1a35838d36343bf9212f99bd41206c52693aadd6e56112395776e2ffad1","sha256:72bdcb1ff52a4791c11e6775aa6bcd068e9cde0a65333c4a630835b84fe666d8"],"state_sha256":"f250e884dcb7161731fc68340f795c303bcfbd3790e2612e13a5b64ba3800b71"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ms5kEh8otuMfE6aF+1MNx4jjaWFobh1P9lHlez4uY1IX/Icr9GaKhnqpdEQMeQRXFCGO2Nc1soIFAJBcu7u0Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T23:37:13.167589Z","bundle_sha256":"4c26e9aa1d3205e4786eb2c9ceb007c4b7e898bd97d1c723e1b4430968fd80d6"}}