Linear probing fine tuning in machine learning pdf. However, despite the widespread use of .

Linear probing fine tuning in machine learning pdf However, despite the widespread use of large language ID vs. Here we analyze the sample complexity of this scheme for regression with linear teachers in several architectures. Verify the effectiveness of LoRA and temperature scaling. TURN uses linear probing and fine-tuning on a refined subset of the training dataset. For vision-language foundation models, we only used their vision encoders. 6% accuracy, with a simi-lar 7% bump over linear probing. This paper (1) analyzes the training Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 1st Linear probing (LP), 2nd Fine-tuning (FT) FT starts with the optimized linear layer (classifier). Jun 17, 2024 · We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. Our exploration into fine-tuning methods, including traditional fine-tuning, linear probing, and their combination, revealed traditional fine-tuning as the superior approach for our use case, as detailed in Table 3. LG] 21 Feb 2022 Apr 4, 2022 · Abstract. Abstract—Based on the success of large-scale visual foundation models like CLIP in various downstream tasks, this paper initially attempts to explore their impact on Long-Tailed Semi-Supervised Learning (LTSSL) by employing the foundation model with three strategies: Linear Probing (LP), Lightweight Fine-Tuning (LFT) and Full Fine-Tuning (FFT). Aug 16, 2021 · We study the performance of federated learning algorithms and their variants in an asymptotic framework. Our ultimate purpose was to use probing to better understand practical production problems and consequently to build better NLU models. Fine-tuning the 6 GLUE tasks takes around 30 GPU hours in total, while probing the 7 tasks (all 12 layers) takes 0. However, despite the widespread use of Sep 13, 2024 · This paper introduces Kolmogorov-Arnold Networks (KAN) as an en-hancement to the traditional linear probing method in transfer learning. In this work, we propose a more accurate and robust alter-native to the second step of the conventional recipe in the context of fine-tuning a large pre-trained model. Feb 21, 2022 · View a PDF of the paper titled Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution, by Ananya Kumar and 4 other authors Demonstrate LP-FT mitigates feature distortion in language models. 2 Related Work Research on evaluating and leveraging sparsity for model pruning has become one of the most signifi-cant topics within the machine learning community. However, despite the widespread use of May 27, 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-of-distribution (OOD) data. 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. It introduces a discriminative adapter for probing ba-sic discriminative abilities in the first stage and performs discriminative fine-tuning in the second stage. 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 What are Probing Classifiers? Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. Through systematic evaluation across seven datasets and six PFT variants, we demonstrate LP-FT's superiority in balancing personalization and generalization. We highlight the limitations of current fine-tuning methods and the challenges of learning ro-bust models. 5% result is +2. 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. 10054v1 [cs. However, despite the widespread use of large We further identify that linear probing excels in preserving robustness from the ro-bust pretraining. Apr 5, 2023 · Ananya Kumar, Stanford Ph. Given Specifically, linear probing, which keeps the encoder frozen and adapts the upcasted original scale features to downstream tasks; decoder probing, adding a lightweight decoder after the frozen encoder to facilitate adaptation; and full fine-tuning, where the encoder and decoder are optimized for downstream tasks. ID vs. In full fine-tuning, all parameters of the LPM are made learnable during training on the downstream tasks. However, one of the most commonly used methods, linear probing, which involves training a linear classifier on top of the frozen features from the Mar 24, 2024 · In this paper, we propose Domain-Aware Fine-Tuning (DAFT), a novel approach that incorporates batch normalization conversion and the integration of linear probing and fine-tuning. Our batch normalization conversion method effectively mitigates feature distortion by reducing modifications to the neural network during fine-tuning. Jul 30, 2023 · Despite the fact that MIM models show good performance on fine-tuning and transfer learning, the linear probing accuracy of these approaches is worse than that of contrastive learning. D. ration). This success is largely attributed to the preservation of pre-trained features, achieved through a near-optimal linear head obtained during LP. From left to right, each column shows the original images and attention maps achieve by linear probing, full fine-tuning, our MP and MP+. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. 3%) and even comparable to fully fine-tuning on certain datasets. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. With the CLIP being proposed, the fine-tuning of language-image pre-training models becomes more flexible and diverse. By leveraging pre-trained modelssuchasResNet-50[2],transferlearningallowsfore(祭)酆cientadaptationto new tasks. 4). We compare FPT against full fine-tuning, linear probing, and state-of-the-art (SOTA) PEFT approaches. Oct 23, 2024 · This framework explains why linear probing helps guide the subsequent fine-tuning process. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. However, one of the most commonly used methods, linear probing, which involves training a linear classifier on top of the frozen features Nov 28, 2022 · I’m not an expert, so please take this with a grain of salt, but based on my experience working with OpenAI’s CLIP, fine-tuning pre-trained OpenAI models works via linear probing. This removes the need for vali-dation searches for the optimization hyper-parameters, re-ducing the computational load for fine-tuning (Table 2), while yielding performances on par with the best learning rates found with validation (Fig. However, despite the widespread use of Dec 10, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. The proposed method, named Weight-Space Ensem-bles for Fine-Tuning then Linear Probing (WiSE-FT-LP), integrates the On ImageNet, we achieve 66. However, despite the widespread use of large We notice that the two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), performs well in central-ized transfer learning, so this paper expands it to federated learning problems. •Prior work studies linear probing (fitting linear head on features) •Fine-tuning is non-convex, trajectory is complicated and has no known closed form even for two-layer linear networks •Tool: leverage invariants that hold throughout process of fine-tuning May 26, 2024 · Our analysis decomposes the NTK matrix into two components, highlighting the importance of the linear head norm alongside the prediction accuracy at the start of the FT stage. We propose a linear regression model, where, for a given client, we theoretically compare the performance When transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating all the model parameters) and linear probing (updating only the last linear layer -- the "head"). Based on this, we propose Robust Linear Initialization (RoLI) for adversarial finetuning, which ini-tializes the linear head with the weights obtained by ad-versarial linear probing to maximally inherit the robust-ness from pretraining. Going beyond conventional linear probing (LP) and fine tuning (FT) strategies, protocols that can effectively control feature distortion, i. OOD:99981231160000-0800 different directions, not just reweighting Pretrained Features Fine-tuning: features for ID examples change in sync with the linear head Feature distortion Head performs poorly on OOD examples Features for OOD examples change less ID OOD Pretrained Features Fine-tuning Linear probing: freezes pretrained features Head performs poorly on OOD examples Pretrained The analysis differentiates between various fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, underscoring their respective implications for specific tasks. Zero-shot prediction is the most common way to evaluate the VLMs, where we directly apply pre-trained VLMs to downstream tasks without any task-specific fine-tuning. 3% accuracy after fine-tuning 322, at MR a bump of 6% over linear probing. , the failure to update features orthogonal to the in-distribution, have been found to Sep 13, 2024 · 1. Probes in the above sense are supervised We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. Abstract Fine-tuning is a common practice in deep learning, achieving excellent general-ization results on downstream tasks using relatively little training data. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. This method leverages the robustness of linear probing and the generalization capability of fine-tuning adapters to handle noisy datasets during training stage. Nov 20, 2025 · For CXR foundation models, we conducted linear probing or fine-tuning of the exponential moving av-erage encoder. We further identify that linear probing excels in preserving robustness from the ro-bust pretraining. Instead of selecting the individual fine-tuned model which achieves the highest accuracy on the held-out validation set, we average the weights of models fine-tuned independently, and refer to the result as a model soup. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based Feb 29, 2024 · Differentially private (DP) machine learning pipelines typically involve a two-phase process: non-private pre-training on a public dataset, followed by fine-tuning on private data using DP optimization techniques. Then, we investigate empirical behaviors and practices of probing through our mathematical framework. representations to all layers [43, 27], and this diversity contributes to its better generalization on downstream fine-tuning. This holds true for both indistribution (ID) and out-of-distribution (OOD) data. However, despite the widespread use of Nov 28, 2024 · In this paper, we propose a label-free prompt-tuning method that leverages the rich visual features of self-supervised learning models (DINO) and the broad textual knowledge of large language models (LLMs) to largely enhance CLIP-based image classification performance using unlabeled images. However, recent studies have Features change orders of magnitude less with LP-FT LP-FT Early stopping does not solve the problem with fine-tuning OOD Acc. arXiv:2202. Although widely used in practice, it is lacking strong theoretical understanding. This method is very fast and eficient in terms of the number of parameters trained, but it can be suboptimal due to its low capacity to Transfer learning has become a cornerstone of modern machine learning, particularly in scenarios with limited labeled data [1]. All models are pre-trained on ImageNet-21K and fine-tuned on ImageNet-1K using the ViT-B/16 model. In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. 6% to 85. 7%. Meanwhile, many studies have revealed that language models are also powerful May 13, 2022 · First, we compare the two popular update methods, full fine-tuning (i. In particular, using ViT-base, we improve the fine-tuning results of the vanilla MAE from 83. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 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. Linear probing Full fine-tuning Epochs of fine-tuning Theory says fine-tuning does worse than linear probing if features good, distribution shift large Jan 14, 2025 · In this paper, we exploit models obtained in Self-Supervised Learning (SSL) to mitigate the impact of noisy labels in FL. These classifiers aim to understand how a model processes and encodes different aspects of input data, such as syntax, semantics, and other linguistic features. It is well known that fine-tuning leads to better accuracy in-distribution (ID). We find that LP is better than FT with extremely few samples, whereas FT outperforms LP as training samples increase. Under review. This paper proposes a new federated learning method called FedLP FT. 2% stronger than the linear probing result (80. , updating only a linear classifier, LP). By leveraging pre-trained models such as ResNet-50 [2], transfer learning allows for efficient adaptation to new tasks. Dec 23, 2024 · We further propose using the output features from those two models as the collaborative target of the decoder. e. Intuitively, the success of fine Abstract Recently, eficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. Molchanov et al. Popular adapters •Cross-Modal Adaptation •Frozen transformers, ORCA, aligning via optimal transport dataset distance •Model Editing Preprint. May 27, 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-of-distribution (OOD) data. Final section: unsupervised probes. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. (2016) explored the sparsity of convolutional neural networks through backpropa-gation and fine-tuning, laying the groundwork for understanding the potential applications of sparsity in resource-eficient inference May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Dec 3, 2023 · End-to-end Fine-tuning (FT) and Linear Probing (LP) are two traditional implementation. 7 GPU hours to cache and 1. Mar 23, 2023 · Advances in the expressivity of pretrained models have increased interest in the design of adaptation protocols which enable safe and effective transfer learning. negative transfer [8, 51] arises especially when downstream tasks are out of the distribution of pre-training data. We designed experiments to see how fine-tuning changes the linguistic capabilities of machine-learning computer-vision deep-learning master-thesis transformers pytorch image-classification transfer-learning linear-probing fine-tuning huggingface vision-transformers zero-shot-transfer prompt-engineering Oct 3, 2024 · s Outline •Fine-Tuning and Adapter Intro •Fine-tuning vs. The most common approaches for transfer learning are linear probing and finetuning. Moreover, with RoBERTa-large, MeZO achieves performance close to standard fine-tuning within 5% gap; with OPT-13B, MeZO outperforms or performs comparably to fine-tuning on 7 out of 11 tasks, despite requiring roughly 12× less memory (Figure 1 and Se Oct 17, 2025 · Original Source Title: Tuning Pre-trained Model via Moment Probing Abstract: Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. prompting, linear probing, etc. The study examines the relationship between the model's feature space during linear probing and the optimization trajectory during fine-tuning. Its enhanced performance is evident from experiments on CIFAR-100, Clothing 1M, and WebVision datasets, demonstrating both improved results and lower computational costs. They show that linear probing creates an improved initialization state for fine-tuning. ABSTRACT This paper presents a robust fine-tuning method designed for pre-trained 3D point cloud models, to enhance feature robustness in downstream fine-tuned models. In the DP setting, it has been observed that full fine-tuning may not always yield the best test accuracy, even for in-distribution data. , updating the entire network, FT) and linear probing (i. Looking at the request 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 A source of valuable insights, but we need to proceed with caution: É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). , 2022), to the FL setting. Robustness to distribution shifts Train Pedestrians using a crosswalk A core challenge for reliable machine learning in the wild Nov 16, 2025 · We propose adapting Linear Probing followed by full Fine-Tuning (LP-FT), a principled centralized strategy for alleviating feature distortion (Kumar et al. Full vs partial fine tuning vs adapting. Figure 1: Exemplar attentive regions of the model trained (a) from scratch, by (b) linear probing, (c) vanilla fine-tuning, and (d) bi-tuning via Eigen-Grad-CAM [38], where only (a) predicts correctly. Linear probing is a technique where you take the second-to-last layer of a NN (so the layer before the output layer) and further tune the weights from the base model using your datasets. 1 Motivation Transfer learning has become a cornerstone of modern machine learning, par-ticularly in scenarios with limited labeled data [1]. However, despite the Jun 17, 2024 · They followed a systematic fine-tuning approach by first fine-tuning the last classification layer (linear probing) and then fine-tuning all layers of the network. We fur-ther confirm the superiority of our method in learning with data at different scales and in handling out-of-distribution samples. . Nevertheless, PALP greatly minimizes the performance gap between white-box tuning methods, such as Adapter or full fine-tuning, andblack-box tuning methods, which is around 7% with baseline linear probing methods, while our approach narrows this gap to nearly 4%. However, despite the widespread use of In addition to Full-model Fine-tuning and linear probing, we re-implement several SOTA efficient fine-tuning methods [6, 14, 7, 8, 15] (originally proposed for pretrained Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. When fine-tuning at MR 482, we achieve 72. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. * Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models. Workshop Transfer Learning for Natural Language Processing Alon Albalak · Colin Raffel · Chunting Zhou · Deepak Ramachandran · Xuezhe Ma · Sebastian Ruder Dec 21, 2022 · Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. A structured seven-stage pipeline for LLM fine-tuning is introduced, covering the complete lifecycle from data preparation to model deployment. Abstract Recently, eficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. Changes to pre-trained features are minimized. However, despite the More excitingly, this 82. Aug 15, 2023 · In this paper, we propose Domain-Aware Fine-Tuning (DAFT), a novel approach that incorporates batch normalization conversion and the integration of linear probing and fine-tuning. 3 CPU hours to probe. Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model. Our starting point is the formulation of federated learning as a multi-criterion objective, where the goal is to minimize each client's loss using information from all of the clients. 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. Generally the setup used for evaluating VLMs is zero-shot prediction and linear probing. In this study, we assess an extensive range of augmentations through linear probing, zero-shot transfer, fine-tuning, and data efficiency experiments and show that: • Visual representations extracted with different augmentations results in substantial variations on downstream classification tasks (up to 18% difference). Nevertheless, MIM pre-training is slower to converge and underperforms in linear probing, mainly due to its lack of discrimination ability. By probing a pre-trained model's internal representations, researchers and data First, we connect probing with the variational bounds of mutual informa-tion (MI) to relax the probe design, equating linear probing with fine-tuning. MeZO consistently outperforms zero-shot, ICL, and linear probing. In linear probing, only the linear readout head is trained on the new task, while the weights of all other layers in the model are frozen at their initial (pretrained) values. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. Our simple and effective framework pre-trained on ImageNet-1K achieves state-of-the-art linear probing and fine-tuning performance. Our analysis presents the following insights: i We notice that the two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), performs well in central-ized transfer learning, so this paper expands it to federated learning problems. vssk fqltz xabeqi dghl kzjd wwwo ehfzd vwklfpm jeouk ekfcauy emay fhyh nbfgisik syim fufs