Linear Projections of Teacher Embeddings for Few-Class Distillation
Fuente:
arXiv
Saved in:
| Main Authors: | Loo, Noel, Iliopoulos, Fotis, Hu, Wei, Vee, Erik |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GUIDE: Guided Initialization and Distillation of Embeddings
by: Trinh, Khoa, et al.
Published: (2025)
by: Trinh, Khoa, et al.
Published: (2025)
CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers
by: Nair, Lakshmi
Published: (2024)
by: Nair, Lakshmi
Published: (2024)
Few-Shot Class-Incremental Learning with Prior Knowledge
by: Jiang, Wenhao, et al.
Published: (2024)
by: Jiang, Wenhao, et al.
Published: (2024)
Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
by: Monsefi, Amin Karimi, et al.
Published: (2026)
by: Monsefi, Amin Karimi, et al.
Published: (2026)
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey
by: Zhang, Jinghua, et al.
Published: (2023)
by: Zhang, Jinghua, et al.
Published: (2023)
NTK-Guided Few-Shot Class Incremental Learning
by: Liu, Jingren, et al.
Published: (2024)
by: Liu, Jingren, et al.
Published: (2024)
An experimental approach on Few Shot Class Incremental Learning
by: Adam, Marinela
Published: (2025)
by: Adam, Marinela
Published: (2025)
Few-shot Class-incremental Fault Diagnosis by Preserving Class-Agnostic Knowledge with Dual-Granularity Representations
by: Yang, Zhendong, et al.
Published: (2025)
by: Yang, Zhendong, et al.
Published: (2025)
Adaptive Locally Linear Embedding
by: Goli, Ali, et al.
Published: (2025)
by: Goli, Ali, et al.
Published: (2025)
Model Merging via Multi-Teacher Knowledge Distillation
by: Dalili, Seyed Arshan, et al.
Published: (2025)
by: Dalili, Seyed Arshan, et al.
Published: (2025)
Few-Shot Class-Incremental Learning with Non-IID Decentralized Data
by: Liu, Cuiwei, et al.
Published: (2024)
by: Liu, Cuiwei, et al.
Published: (2024)
Few-Shot Class Incremental Learning via Robust Transformer Approach
by: Paeedeh, Naeem, et al.
Published: (2024)
by: Paeedeh, Naeem, et al.
Published: (2024)
An Efficient Memory Module for Graph Few-Shot Class-Incremental Learning
by: Li, Dong, et al.
Published: (2024)
by: Li, Dong, et al.
Published: (2024)
Teaching the Teacher: The Role of Teacher-Student Smoothness Alignment in Genetic Programming-based Symbolic Distillation
by: Dhar, Soumyadeep, et al.
Published: (2025)
by: Dhar, Soumyadeep, et al.
Published: (2025)
Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
by: Fang, Luyang, et al.
Published: (2026)
by: Fang, Luyang, et al.
Published: (2026)
DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher
by: Zhong, Yisheng, et al.
Published: (2026)
by: Zhong, Yisheng, et al.
Published: (2026)
Toward Student-Oriented Teacher Network Training For Knowledge Distillation
by: Dong, Chengyu, et al.
Published: (2022)
by: Dong, Chengyu, et al.
Published: (2022)
From Teacher to Student: Tracking Memorization Through Model Distillation
by: Singh, Simardeep
Published: (2025)
by: Singh, Simardeep
Published: (2025)
Distilling Linearized Behavior into Non-Linear Fine-Tuning for Effective Task Arithmetic
by: Sommariva, Thomas, et al.
Published: (2026)
by: Sommariva, Thomas, et al.
Published: (2026)
TabDistill: Distilling Transformers into Neural Nets for Few-Shot Tabular Classification
by: Dissanayake, Pasan, et al.
Published: (2025)
by: Dissanayake, Pasan, et al.
Published: (2025)
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student Architectures
by: Binici, Kuluhan, et al.
Published: (2024)
by: Binici, Kuluhan, et al.
Published: (2024)
Heuristic Methods are Good Teachers to Distill MLPs for Graph Link Prediction
by: Qin, Zongyue, et al.
Published: (2025)
by: Qin, Zongyue, et al.
Published: (2025)
Aggregated f-average Neural Network applied to Few-Shot Class Incremental Learning
by: Vu, Mathieu, et al.
Published: (2023)
by: Vu, Mathieu, et al.
Published: (2023)
On Teacher Hacking in Language Model Distillation
by: Tiapkin, Daniil, et al.
Published: (2025)
by: Tiapkin, Daniil, et al.
Published: (2025)
Low-Dimensional Federated Knowledge Graph Embedding via Knowledge Distillation
by: Zhang, Xiaoxiong, et al.
Published: (2024)
by: Zhang, Xiaoxiong, et al.
Published: (2024)
Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model
by: Chen, Jinyin, et al.
Published: (2024)
by: Chen, Jinyin, et al.
Published: (2024)
When Are Teacher Tokens Reliable? Position-Weighted On-Policy Self-Distillation for Reasoning
by: Liu, Xiaogeng, et al.
Published: (2026)
by: Liu, Xiaogeng, et al.
Published: (2026)
Group Relative Knowledge Distillation: Learning from Teacher's Relational Inductive Bias
by: Li, Chao, et al.
Published: (2025)
by: Li, Chao, et al.
Published: (2025)
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
by: Golowich, Noah, et al.
Published: (2024)
by: Golowich, Noah, et al.
Published: (2024)
Parameter-Efficient Token Embedding Editing for Clinical Class-Level Unlearning
by: Hou, Iyad Ait, et al.
Published: (2026)
by: Hou, Iyad Ait, et al.
Published: (2026)
Beyond Linearity in Attention Projections: The Case for Nonlinear Queries
by: Karbevski, Marko
Published: (2026)
by: Karbevski, Marko
Published: (2026)
Efficient and Robust Knowledge Distillation from A Stronger Teacher Based on Correlation Matching
by: Niu, Wenqi, et al.
Published: (2024)
by: Niu, Wenqi, et al.
Published: (2024)
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
by: Ji, Guangda, et al.
Published: (2020)
by: Ji, Guangda, et al.
Published: (2020)
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
by: Kim, Shiwon, et al.
Published: (2025)
by: Kim, Shiwon, et al.
Published: (2025)
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
by: Zuo, Zhiwei, et al.
Published: (2024)
by: Zuo, Zhiwei, et al.
Published: (2024)
Linear Dimensionality Reduction for Word Embeddings in Tabular Data Classification
by: Ressel, Liam, et al.
Published: (2025)
by: Ressel, Liam, et al.
Published: (2025)
Distilling Genomic Models for Efficient mRNA Representation Learning via Embedding Matching
by: Haidari, Rasched, et al.
Published: (2026)
by: Haidari, Rasched, et al.
Published: (2026)
Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
by: Kim, Byunghyun, et al.
Published: (2024)
by: Kim, Byunghyun, et al.
Published: (2024)
Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference
by: Marsh, Matthew, et al.
Published: (2026)
by: Marsh, Matthew, et al.
Published: (2026)
Class Incremental Fault Diagnosis under Limited Fault Data via Supervised Contrastive Knowledge Distillation
by: Zhang, Hanrong, et al.
Published: (2025)
by: Zhang, Hanrong, et al.
Published: (2025)
Similar Items
-
GUIDE: Guided Initialization and Distillation of Embeddings
by: Trinh, Khoa, et al.
Published: (2025) -
CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers
by: Nair, Lakshmi
Published: (2024) -
Few-Shot Class-Incremental Learning with Prior Knowledge
by: Jiang, Wenhao, et al.
Published: (2024) -
Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation
by: Monsefi, Amin Karimi, et al.
Published: (2026) -
Few-shot Class-incremental Learning for Classification and Object Detection: A Survey
by: Zhang, Jinghua, et al.
Published: (2023)