We introduce DICHUBTT, the dissolution of information to oppress Hubert, a model for the highest-based learning process. Unlike existing decorative methods depending on Ungler-Aned and Performative Models, Discuhubert Facilities of Hubert's Terativation Mechanism by directly entering the original model and student model. This exchange allows the learner to be trained using the same SSL aim used when training before training, eliminating the need for additional modules or construction issues. The test results in the Superb Show in DICHUBT is constantly plaguing the existing mechanisms, improving the performance of the Pename recognition for more than 21% and Asr to work over 14%. In addition, DICHUBERT indicates the operation of competition in all many tasks, highlighting its clear profit.
- 40 Cernegie Mellon University
- ‡ Meta
- ** Work done while in Apple
