{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3NGOBYHXLIZ6LBXJH2S5FK7YCT","short_pith_number":"pith:3NGOBYHX","schema_version":"1.0","canonical_sha256":"db4ce0e0f75a33e586e93ea5d2abf814e6fde0f57b25d67d1f8d33810a1e2e93","source":{"kind":"arxiv","id":"1812.11675","version":4},"attestation_state":"computed","paper":{"title":"Soft Autoencoder and Its Wavelet Adaptation Interpretation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fenglei Fan, Ge Wang, Mengzhou Li, YueYang Teng","submitted_at":"2018-12-31T02:20:05Z","abstract_excerpt":"Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers from the model opaqueness as well. In this paper, we propose a new type of convolutional autoencoders, termed as Soft Autoencoder (Soft-AE), in which the activation functions of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1812.11675","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-31T02:20:05Z","cross_cats_sorted":["eess.SP","stat.ML"],"title_canon_sha256":"52f7ab16e59433496bdedc8d187de71540438ea53b02d0ea77adc6c907ed5ab4","abstract_canon_sha256":"ae7b699233d0f9c2f37a595caf69a6c75418ae19a1d125792092fc3c678f5319"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:04:25.780819Z","signature_b64":"JGNLwJxntKVyZLyHsOIXl4TimPTlsEvgx1EIyGUTsOSEpteWvdVCZk35802Mis8PLy/6a4bpGzw+94ZoBnAWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db4ce0e0f75a33e586e93ea5d2abf814e6fde0f57b25d67d1f8d33810a1e2e93","last_reissued_at":"2026-07-05T02:04:25.780458Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:04:25.780458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Soft Autoencoder and Its Wavelet Adaptation Interpretation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Fenglei Fan, Ge Wang, Mengzhou Li, YueYang Teng","submitted_at":"2018-12-31T02:20:05Z","abstract_excerpt":"Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers from the model opaqueness as well. In this paper, we propose a new type of convolutional autoencoders, termed as Soft Autoencoder (Soft-AE), in which the activation functions of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11675","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1812.11675/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1812.11675","created_at":"2026-07-05T02:04:25.780514+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.11675v4","created_at":"2026-07-05T02:04:25.780514+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11675","created_at":"2026-07-05T02:04:25.780514+00:00"},{"alias_kind":"pith_short_12","alias_value":"3NGOBYHXLIZ6","created_at":"2026-07-05T02:04:25.780514+00:00"},{"alias_kind":"pith_short_16","alias_value":"3NGOBYHXLIZ6LBXJ","created_at":"2026-07-05T02:04:25.780514+00:00"},{"alias_kind":"pith_short_8","alias_value":"3NGOBYHX","created_at":"2026-07-05T02:04:25.780514+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT","json":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT.json","graph_json":"https://pith.science/api/pith-number/3NGOBYHXLIZ6LBXJH2S5FK7YCT/graph.json","events_json":"https://pith.science/api/pith-number/3NGOBYHXLIZ6LBXJH2S5FK7YCT/events.json","paper":"https://pith.science/paper/3NGOBYHX"},"agent_actions":{"view_html":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT","download_json":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT.json","view_paper":"https://pith.science/paper/3NGOBYHX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.11675&json=true","fetch_graph":"https://pith.science/api/pith-number/3NGOBYHXLIZ6LBXJH2S5FK7YCT/graph.json","fetch_events":"https://pith.science/api/pith-number/3NGOBYHXLIZ6LBXJH2S5FK7YCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT/action/storage_attestation","attest_author":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT/action/author_attestation","sign_citation":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT/action/citation_signature","submit_replication":"https://pith.science/pith/3NGOBYHXLIZ6LBXJH2S5FK7YCT/action/replication_record"}},"created_at":"2026-07-05T02:04:25.780514+00:00","updated_at":"2026-07-05T02:04:25.780514+00:00"}