{"work":{"id":"ca0a2fcc-b704-4e8a-8977-b8bcba691059","openalex_id":"https://openalex.org/W4317672249","doi":"10.48550/arxiv.2301.08243","arxiv_id":"2301.08243","raw_key":null,"title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","authors":null,"authors_text":"Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas","year":2023,"venue":"cs.CV","abstract":"This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.","external_url":"https://arxiv.org/abs/2301.08243","cited_by_count":18,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"2301.08243","created_at":"2026-05-10T11:15:10.328083+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":true,"display_title":"Assran et al","render_title":"Assran et al"},"hub":{"state":{"work_id":"ca0a2fcc-b704-4e8a-8977-b8bcba691059","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":24,"external_cited_by_count":18,"distinct_field_count":6,"first_pith_cited_at":"2026-02-06T18:07:20+00:00","last_pith_cited_at":"2026-07-07T12:19:54+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-19T06:20:04.257112+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":4}],"polarity_counts":[{"context_polarity":"background","n":4}],"runs":{},"summary":{},"graph":{},"authors":[]}}