Paper List | 一文看 AAAI 2021 模型壓縮 paper
【未經允許，請勿轉載】
如果有錯誤之處，非常感謝大家提出，我也是在學習中。
AAAI 2021 – Important Dates
August 15 – August 30, 2020:
Authors register on the AAAI web site
September 1, 2020:
Electronic abstracts due at 11：59 PM UTC-12 （anywhere on earth）
September 9, 2020:
Electronic papers due at 11：59 PM UTC-12 （anywhere on earth）
September 29, 2020:
Abstracts AND full papers due for revisions of rejected NeurIPS/EMNLP submissions by 11：59 PM UTC-12 （anywhere on earth）
AAAI-21 Reviewing Process： Two-Phase Reviewing and NeurIPS/EMNLP Fast Track Submissions
November 3-5, 2020:
Author Feedback Window （anywhere on earth）
December 1, 2020:
Notification of acceptance or rejection
AAAI 2021 Pruning
TransTailor： Pruning the Pre-Trained Model for Improved Transfer Learning
Provable Benefits of Overparameterization
in Model Compression： From Double Descent to Pruning Neural Networks
Linearly Replaceable Filters
for Deep Network
Channel Pruning
Compressing Deep Convolutional Neural Networks by Stacking
Low-Dimensional Binary Convolution Filters
Tied Block Convolution： Leaner and Better CNNs with
Shared Thinner Filters
Revisiting Dominance
Pruning
in Decoupled Search
CAKES：
Channel-Wise
Automatic Kernel Shrinking for Efficient 3D Networks
DPFPS：
Dynamic and Progressive Filter Pruning
for Compressing Convolutional Neural Networks
from Scratch
OPQ： Compressing Deep Neural Networks with
One-Shot Pruning-Quantization
AutoLR： Layer-Wise Pruning and
Auto-Tuning of Learning Rates
in Fine-Tuning of Deep Networks
Accurate and Robust
Feature Importance Estimation
under
Distribution Shifts
Winning Lottery Ticket
s in Deep Generative Models
Slimmable
Generative Adversarial Networks
Towards Faster Deep Collaborative Filtering via Hierarchical Decision Networks
Quantization
Optimizing Information Theory Based Bitwise Bottlenecks for Efficient Mixed-Precision Activation Quantization
Scalable Verification of Quantized Neural Networks
Stochastic Precision Ensemble：
Self-Knowledge Distillation
for Quantized Deep Neural Networks
Weakly Supervised Deep Hyperspherical Quantization for
Image Retrieval
FracBits：
Mixed Precision
Quantization via Fractional Bit-Widths
Distribution Adaptive
INT8
Quantization for Training CNNs
TRQ： Ternary Neural Networks with Residual Quantization
Training
Binary
Neural Network
without Batch Normalization
for Image Super-Resolution
SA-BNN： State-Aware
Binary
Neural Network
Post-training Quantization
with Multiple Points：
Mixed Precision
without Mixed Precision
Any-Precision Deep Neural Networks
Distillation
Show， Attend and Distill： Knowledge Distillation via Attention-Based Feature Matching
PSSM-Distil
： Protein Secondary Structure Prediction （PSSP） on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning
Cross-Layer Distillation with Semantic Calibration
Harmonized Dense Knowledge Distillation Training for Multi-Exit Architectures
Universal Trading for Order Execution with Oracle Policy Distillation
Diverse Knowledge Distillation for End-to-End Person Search
Distilling Localization for Self-Supervised Representation Learning
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis
Progressive Network Grafting for Few-Shot Knowledge Distillation
Robust Knowledge Transfer via Hybrid Forward on the Teacher-Student Mode
Peer Collaborative Learning for Online Knowledge Distillation
