Compression Team
Team delivers the methods of Deep Learning models compression
BUSINESS CASES
RAM, Energy, CPU/GPU lower consumption
On-device models transfer
Models usage on low-bit CPU
Speeding up calculations
TASKS CLASSES
Low-bit and Post-Training Quantization for complex architectures (Img-to-img, Transformer)
Knowledge Distillation & Pruning
Neural Architecture Search & SuperNets
CURRENT CHALLENGES
Methods for complex architectures such as Transformers and img2img
Unification of methods usability, transition to end-to-end optimization and on-device transfer
ACHIEVEMENTS
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