Progressive Multi-Ancestor Bit-Depth Distillation
Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computational, memory, and energy resources. Prior works that rely on simultaneous conversion from floating-point high-precision (FP32) to integer low-precision (INT4) representations and distillation into smaller models suffer from unstable training and drastic degradation of prediction performance. To address these limitations, we propose a unified framework, known as \textbf{P}rogressive \textbf{M}ulti-\textbf{A}ncestor \textbf{B}it-depth \textbf{D}istillation (PMABD), that progressively compresses the network while transferring knowledge through...
| Authors | Adil Mubashir Chaudhry, Osama Ahmad, Zubair Khalid et al. |
|---|---|
| Primary category | cs.LG |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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