From 5096bb77e7e0f9644e5732839e1c1eeb66523946 Mon Sep 17 00:00:00 2001 From: Tanay Narshana Date: Sat, 15 Apr 2023 04:04:18 +0530 Subject: [PATCH] Updated code links for DFPC and TVSPrune papers --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3692f17..e7b4a49 100644 --- a/README.md +++ b/README.md @@ -49,8 +49,8 @@ A survey of structured pruning can be found at this link: [arxiv](https://arxiv. | [A Unified Framework for Soft Threshold Pruning](https://openreview.net/forum?id=cCFqcrq0d8) | ICLR | `W` | [PyTorch(Author)](https://github.com/Yanqi-Chen/LATS) | | [CrAM: A Compression-Aware Minimizer](https://openreview.net/forum?id=_eTZBs-yedr) | ICLR | `W` | - | | [Trainability Preserving Neural Pruning](https://openreview.net/forum?id=AZFvpnnewr) | ICLR | `F` | - | -| [DFPC: Data flow driven pruning of coupled channels without data](https://openreview.net/forum?id=mhnHqRqcjYU) | ICLR | `F` | [PyTorch(Author)](https://drive.google.com/drive/folders/18eRYzWnB_6Qq0cYiSzvyOgicqn50g3-m) | -| [TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning](https://openreview.net/forum?id=sZI1Oj9KBKy) | ICLR | `F` | [PyTorch(Author)](https://github.com/tvsprune/TVS_Prune) | +| [DFPC: Data flow driven pruning of coupled channels without data](https://openreview.net/forum?id=mhnHqRqcjYU) | ICLR | `F` | [PyTorch(Author)](https://github.com/TanayNarshana/DFPC-Pruning) | +| [TVSPrune - Pruning Non-discriminative filters via Total Variation separability of intermediate representations without fine tuning](https://openreview.net/forum?id=sZI1Oj9KBKy) | ICLR | `F` | [PyTorch(Author)](https://github.com/chaimurti/TVSPrune) | | [HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers](https://openreview.net/forum?id=D7srTrGhAs) | ICLR | `F` | - | | [MECTA: Memory-Economic Continual Test-Time Model Adaptation](https://openreview.net/forum?id=N92hjSf5NNh) | ICLR | `F` | - | | [DepthFL : Depthwise Federated Learning for Heterogeneous Clients](https://openreview.net/forum?id=pf8RIZTMU58) | ICLR | `F` | - |