Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning
Authors: Yubei Chen, Adrien Bardes, ZENGYI LI, Yann LeCun
TMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | m "n$"o! 6 $ ./012345 p -q #!-6q3%!7 ' ! ' 7-! '!"' o,+3r6!+ rr #" 3 -"! &8 9 : ; < > < ? B G HH HH > D G HH stuvwxytz{|u}t~ ~ FB d > ; >; ; DG > u~xzvt {u}t~ ~ FB ; < > b >; B D D F B > > = ; >; < > b< > cdc efdef; D... FGHIJKFLMNGOFPQPQRSA TUVTU ? = ?= = DB ? WXYGPJLHFYMGOFPQPQRSA = > ? Z ?= A D [ D S A ? ? \ = ?= > ? Z> ? ]V] _V _= D |
| Researcher Affiliation | Academia | The author affiliation and email information is severely corrupted and unreadable. Thus, it is impossible to classify the affiliation type. Returning 'Academia' as a default placeholder due to insufficient information. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. The text is too corrupted to identify any structured algorithmic descriptions. |
| Open Source Code | No | The paper does not provide any concrete access to source code. There are no explicit statements about code release or links to repositories. The text is too corrupted to confirm. |
| Open Datasets | No | The paper does not provide concrete access information for any publicly available or open datasets. Dataset names, if present, are too corrupted to identify, and no links or formal citations are provided. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology). While there are table-like structures that might contain related numerical data, they are too corrupted to infer split details. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running experiments. The text is too corrupted to identify any such information. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). The text is too corrupted to identify any such information. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. The text is too corrupted to identify any such information. |