Linear Probing Transfer Learning. Future research should explore advanced techniques that strike a bala

Future research should explore advanced techniques that strike a balance between fine-tuning and linear probing to improve transfer learning capabilities. Transfer learning addresses this challenge by utilizing pre-trained models to tackle new tasks with limited labeled data. In particular, we consider the use of stochastic gradient descent (SGD) on a linear model initialized with pretrained weights and using a small training data set from the target distribution. Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. Analyzing Linear Probing Why the degree of independence matters. Our investigation reveals that existing model probing methods perform well for the easy case when the source domain (where models are pre-trained) and the Then, without the episodic emulation, the proposed novel framework, Transductive Linear Probing (TLP), directly transfers pretrained node embeddings for nodes in novel classes learned from Graph Contrastive Learning (GCL) methods [17–23], and fine-tunes a separate linear classifier with the support set to predict labels for unlabeled nodes. Abstract Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. 1 Transfer learning Full fine-tuning requires more computational resources but usually achieves better results because it allows updating the model’s understanding of both low-level and high-level features. This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Their fine-tuning strategy consisted of first training only the last classification layer (linear probing) and then fine-tuning some of the CNN layers with a smaller learning rate. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Towards Better Understanding of Domain Shift on Linear-Probed Visual Foundation Models Oct 1, 2022 · Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one. Sep 12, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. py, and then train a logistic regression classifier using log_reg_sk. We propose a log-likelihood ratio Jan 28, 2025 · We notice that the two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), performs well in centralized transfer learning, so this paper expands it to federated learning problems. Then we summarize the framework’s shortcomings, as well as improvements and advances. 3. 4%, 1. Aug 1, 2024 · To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Jun 14, 2023 · Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations Sep 26, 2024 · Linear probing then fine-tuning (LP-FT) significantly improves language model fine-tuning; this paper uses Neural Tangent Kernel (NTK) theory to explain why. Despite encouraging results from recent developments in transfer learning for adapting pre-trained model to downstream tasks, the performance of model probing is still lagging behind the state-of-the-art parameter efficient tuning methods. When transferring to a target task, a new output head is trained on top of the feature extractor (downstream t Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. The research demonstrates that the LLR score correlates with the proportion of out-of-domain (OOD) data in the dataset and can effectively predict model performance. https://lnkd. Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning: Paper and Code. Game designers created curricula using combinations of several discrete modifications to the basic versions of games such as Space Invaders, Breakout and Freeway, making them progressively more challenging for human players. In the present work, we re Critically, on domain-specific datasets (Galaxy10, Food101), LeJEPA outperforms DINOv2-based transfer learning when pretrained directly on target data. By Apr 7, 2023 · Linear probing (LP) (and k -NN) on the upstream dataset with labels (e. Aug 15, 2024 · In this work we address a shortcoming of linear probing — it is not very strongly correlated with the performance of the models finetuned end-to-end— the latter often being the final objective in transfer learning— and, in some cases, catastrophically misestimates a model’s potential. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Despite years of successful applications, transfer learning igure 1. To address this, we propose This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Linear probing is a scheme in computer programming for resolving collisions in hash tables, data structures for maintaining a collection of key–value pairs and looking up the value associated with a given key. We propose a log-likelihood ratio Sep 25, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Then, without the episodic emulation, the proposed novel framework, Transductive Linear Probing (TLP), directly transfers pretrained node embeddings for nodes in novel classes learned from Graph Contrastive Learning (GCL) methods [17–23], and fine-tunes a separate linear classifier with the support set to predict labels for unlabeled nodes. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. We demonstrate how this Nov 16, 2019 · The probing task is designed in such a way to isolate some linguistic phenomena and if the probing classifier performs well on the probing task we infer that the system has encoded the linguistic phenomena in question. In this work, we conduct the first empirical comparative study of transfer learning for UATR, evaluating multiple pretrained audio models originating from diverse audio domains. Apr 4, 2022 · Abstract. Jan 7, 2024 · Linear probing preserves the pre-trained features but may not achieve as good performance as fine-tuning on significantly different tasks. Despite years of successful applications, transfer learning practice often relies on ad-hoc solutions, while theoretical Oct 5, 2016 · Neural network models have a reputation for being black boxes. Jun 9, 2021 · Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. py. New Stuf ! Aug 15, 2024 · In this work we address a shortcoming of linear probing — it is not very strongly correlated with the performance of the models finetuned end-to-end— the latter often being the final objective in transfer learning— and, in some cases, catastrophically misestimates a model’s potential. Our investigation reveals that existing model probing methods perform well for the easy case when the source domain (where models are pre-trained) and the Dec 10, 2024 · Parameter-efficient transfer learning for NLP. This helps us better understand the roles and dynamics of the intermediate layers. Sep 13, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an en-hancement to the traditional linear probing method in transfer learning. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head Dec 11, 2022 · Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. 客服邮箱： service@x-mol. 8k次，点赞10次，收藏40次。本文详细介绍CLIP模型原理，包括对比学习目标、模型结构、训练数据集等，并通过zero-shot推理与linear probe分类任务验证模型性能。 May 26, 2024 · The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out Jul 27, 2023 · By designing a specially curated T (x) in our linear adjustment model, we show that this spurious correlation can be removed, leading to substantially improved classifiers. While several studies address the problem of what to transfer, the very important question of when to answer remains mostly unanswered, especially from a theoretical point-of-view for regression problems. Visual prompting, a state-of-the-art parameter-efficient transfer learning method, can significantly improve the performance of out-of-distribution tasks. com 官方微信：X-molTeam2 邮编：100098 地址：北京市海淀区知春路56号中航科技大厦 For linear probing, you can first extract model features using unlabeled_extrapolation/extract_features. ABSTRACT Deep learning has significantly advanced image analysis across diverse domains but often depends on large, annotated datasets for success. Linear Probing Count Sketches We didn’t get there last time, and there’s lots of generalizable ideas here. , ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate the quality of visual representations learned via self-supervised learning (SSL). Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Dec 30, 2024 · Ease of Transfer Learning Pretrained models can be easily fine-tuned or adapted using techniques like linear probing, making them versatile for a variety of use cases. The results suggest that linear probing with pretrained audio embeddings is a promising direction for efficient and scalable underwater acoustic classification. Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. (a) Whereas LINEAR utilizes only the last layer for trans-fer learning, (b) HEAD2TOE selects the most useful features from the entire network and trains a linear hea network. Oct 22, 2022 · The delta to is due to transfer learning via agent network parameter initialisation. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. Jun 17, 2023 · We then further improve the performance of fair transfer learning by introducing multi-feedback and Linear-Probing, then Fine-Tuning the training algorithm. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations linear probing在很多SSL方法里也有用到，一个简单的线性分类器，只训练detached掉的特征，通过这个简单分类器的结果来衡量特征表示的质量。 作为一个弱分类器，linear probing没有额外的区分能力，只能忠实的反应特征本来的质量。 Abstract This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. In this review, we trace the rapid evolution of gLMs, analyze Aug 1, 2024 · To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. 48 FID, 1. In the present work, we re-think a In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. On the other hand, linear probing, a standard transfer learning method, can sometimes become the best approach. This paper proposes a new federated learning method called FedLP + FT. This oversight limits our comprehensive Jan 14, 2025 · In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. Meta learning has been the most popular solution for few-shot learning problem. Conference paper / Print Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-Shot In-Context Learners (English) Cho, Hyunsoo / Kim, Hyuhng Joon / Kim, Junyeob / Lee, Sang-Woo / Lee, Sang-goo / Yoo, Kang Min / Kim, Taeuk In: 37th AAAI Conference on Artificial Intelligence (AAAI-23) ; Volume 11, part 1 of 2 ; 12709-12718 ; 2023 Feb 23, 2022 · Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. Oct 22, 2022 · We evaluate the use of original game curricula supported by the Atari 2600 console as a heterogeneous transfer benchmark for deep reinforcement learning agents. Jan 28, 2025 · We notice that the two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), performs well in centralized transfer learning, so this paper expands it to federated learning problems. . y and distort the pretrained features. This challenges the transfer learning paradigm and demonstrates that principled SSL can unlock effective in-domain pretraining—previously considered impractical for small datasets. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. In addition, we explore two popular methods to transfer to downstream tasks: linear probing, which updates only the last classification layers, and fine-tuning, which updates all model parameters. Nov 15, 2025 · 文章浏览阅读5. An early usage of probing tasks can be found in Shi et. al (2016) Does String-Based Neural MT Learn Source Syntax? Abstract We study the problem of transfer learning and fine-tuning in linear models for both regression and binary classification. Sep 25, 2024 · This paper uses NTK theory to analyze the training dynamics of the two-stage “linear probing then fine-tuning” (LP-FT) method, and tries to explain why LP-FT consistently outperforms LP or FT alone for both ID and OOD data. White bars indicate zero-shot transfer performance of default game agents. Extensive experiments on ImageNet-1k demonstrate that Sorcen outperforms the previous Unified SSL SoTA by 0. Sep 16, 2023 · Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. 6 days ago · Transfer Learning (TL) offers a promising alternative to mitigate this limitation. Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Oct 1, 2022 · A new theoretical framework for the problem of parameter transfer for the linear model is proposed. in/gbveukPf Transfer Learning (TL) offers a promising alternative to mitigate this limitation. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing overfitting. However, the existing literature predominantly focuses on transductive few-shot node classification, neglecting Meta learning has been the most popular solution for few-shot learning problem. Fourth Moment Bounds Another approach for estimating frequencies. However, transductive linear probing shows that fine-tuning a simple linear classification head after a pretrained graph neural networks can outperforms most of the sophisticated-designed graph meta learning algorithms. Use a concentration inequality to show that the data structure’s output is close to its expectation. Initially, linear probing (LP) optimizes only the linear head of the model, after which fine-tuning (FT) updates the entire model, including the feature extractor and the linear head. Despite encouraging results from recent developments in transfer learning for adapting pre-trained model to downstream tasks the performance of model probing is still lagging behind the state-of-the-art parameter efficient tuning methods. 76%, and 1. Conference paper / Print Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-Shot In-Context Learners (English) Cho, Hyunsoo / Kim, Hyuhng Joon / Kim, Junyeob / Lee, Sang-Woo / Lee, Sang-goo / Yoo, Kang Min / Kim, Taeuk In: 37th AAAI Conference on Artificial Intelligence (AAAI-23) ; Volume 11, part 1 of 2 ; 12709-12718 ; 2023 21 hours ago · Following their success in natural language processing and protein biology, pretrained large language models have started appearing in genomics in large numbers. Linear probing, Feb 23, 2022 · Probing transfer learning with a model of synthetic correlated datasets, Gerace, Federica, Saglietti, Luca, Sarao Mannelli, Stefano, Saxe, Andrew, Zdeborová, Lenka 1 Introduction Learning visual representations is a critical step towards solving many kinds of tasks, from supervised tasks such as image classification or object detection, to reinforcement learning. However, despite the widespread use of large Then, without the episodic emulation, the proposed novel framework, Transductive Linear Probing (TLP), directly transfers pretrained node embeddings for nodes in novel classes learned from Graph Contrastive Learning (GCL) methods [17–23], and fine-tunes a separate linear classifier with the support set to predict labels for unlabeled nodes. Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. Despite years of successful applications, transfer learning practice often relies on ad-hoc solutions, while theoretical understanding of these procedures is still limited. Consequently, the model may not adapt as well to the Probing by linear classifiers This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. Ever since the early successes of deep reinforcement learning [36], neural networks have been widely adopted to solve pixel-based reinforcement learning tasks such as arcade games [6], physical We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective mod-ification to probing approaches. A joint study by Tsinghua University, IBM, and CUHK proposes a Log-Likelihood Ratio (LLR) scoring method based on visual prompt approximation to evaluate the advantage of visual prompting (VP) over linear probing (LP). 当需要快速评估预训练模型表征能力时，可以选择Linear probing；当目标任务与预训练任务相似且数据集较大时，可以选择Finetune；当需要参数高效且任务独立的迁移学习方法时，可以选择Adapter；而当希望避免微调模型参数且任务灵活性要求较高时，可以选择Prompt。 Sep 3, 2024 · Adapting pre-trained models to new tasks can exhibit varying effectiveness across datasets. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits f both fine-tuning and linear probing. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, Abstract This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. In contrast, linear probing requires less computational resources but offers less flexibility since only the last layer is adjusted. 53% on linear probing, unconditional image generation, few-shot learning, and transfer learning, respectively, while being 60. It is shown that the quality of transfer for a new input vector depends on its representation in an eigenbasis involving the parameters of the problem. D. Use a sum of indicator variables and linearity of expectation to prove that, on expectation, the data structure is pretty close to correct. Apr 4, 2022 · In this short article, we first define the probing classifiers framework, taking care to consider the various involved components. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 2790-2799. Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discriminating features. Apr 1, 2017 · Transfer learning has been the cornerstone of adaptation of pre-trained models to several downstream tasks, however, conventionally were limited to only full fine-tuning (FF) and linear probing. However, recent studies have Apr 5, 2023 · Ananya Kumar, Stanford Ph. 8% more efficient. Let’s go exploring! Linear Probing A simple and lightning fast hash table implementation. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. However, the existing literature predominantly focuses on transductive few-shot node classification, neglecting the widely studied inductive setting in the broader few-shot learning community. This method has been extensively analyzed and enhanced [50, 46, 16, 26]. These genomic language models (gLMs), trained on diverse DNA and RNA sequences, promise improved performance on a variety of downstream prediction and understanding tasks. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. However, despite the widespread use of large Jun 9, 2021 · Probing transfer learning with a model of synthetic correlated datasets: Paper and Code. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head Sep 14, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. This allows Abstract Despite encouraging results from recent developments in transfer learning for adapting pre-trained model to down-stream tasks, the performance of model probing is still lag-ging behind the state-of-the-art parameter efficient tuning methods. Linear probing is a collision resolution method for hash tables that finds empty slots sequentially; it ensures high cache efficiency and constant-time performance with 5-wise independent hashing. The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. They allow us to u Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 1 Linearly adjusted transfer May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 2 Linear adjustment model for the logistic model 2. g. Run multiple copies of the data structure in parallel to amplify the success probability.

cayabdg
ds6im
ln2x0o
nc6h0o4e
8qay8ews
xlenke
piiufqklr
8ld7k4
5pmhdl7iv
5p7pwqcsd