Linear probing fine tuning example. However, despite the widespread use of .

Linear probing fine tuning example The theoretical results are supported by empirical evaluations on various benchmarks and models. Despite CLIP not being trained for these specific tasks, it outperforms a ResNet-50 with a linear probe. A structured seven-stage pipeline for LLM fine-tuning is introduced, covering the complete lifecycle from data preparation to model deployment. However, despite the The analysis differentiates between various fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches, underscoring their respective implications for specific tasks. While coarse, these fea-tures can affect the best model to fine-tune, since a mismatch between fine-tuning dataset size and pre-trained model, or input dimensions, or number of classes, can influence the success of downstream performance. You can download them from this link. finetune: If fine-tuning, this should be True. Oct 3, 2024 · Few-Shot Parameter-Efficient Fine- Tuning is Better and Cheaper than In- Context Learning Liu et al Z22 Before: Frozen Models/Linear Probing We previously discussed freezing our model, and using just some trainable heads •E. Input Resolution: Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. In the CLIP paper, linear probe ResNet-50 was compared to zero-shot CLIP. To run the fine-tuning experiment, execute the following script: Linear probing preceding fine-tuning is irreplaceable, as there is an extremely large performance gap between linear probing and fine-tuning in the presence of noisy labels. Feb 21, 2022 · 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 of both fine-tuning and 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。 First, we compare the two popular up-date methods, full fine-tuning (i. Many of these protocols use the learned representation to solve classification tasks, for example, through linear probing, end-to-end fine-tuning, or by evaluating the embedding representation with a kNN classifier. 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). After initializing with a pretrained model, two popular transfer methods are fine-tuning (running gradient descent on all the model parameters), and linear probing (tuning the head but freezing lower layers). , 2020),111Probing is commonly used Features change orders of magnitude less with LP-FT LP-FT Early stopping does not solve the problem with fine-tuning OOD Acc. Abstract This work explores the comparative eficacy of full fine-tuning, linear probing, and Parameter-Eficient Fine-Tuning (PEFT) techniques, with a focus on Low-rank adaptation (LoRA), in training models for natural language processing tasks such as sentiment classification, paraphrase detection, and semantic textual similarity. The study examines the relationship between the model's feature space during linear probing and the optimization trajectory during fine-tuning. In the ID setting, it is well known that fine-tuning leads to better accuracy than linear probing (Kornblith et al. Jun 19, 2025 · Today, we’ll bring all that theory to life by fine-tuning CLIP on a small image-text dataset and exploring its performance using zero-shot probing and classification tasks. arXiv OpenReview •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 Aug 6, 2025 · Title: Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective Abstract: The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Dec 10, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 1, FMoW geo-shift, ImageNetV2, ImageNet-R, ImageNet-A, ImageNet-Sketch Mar 19, 2025 · Linear probing fine-tunes only the classification branch while freezing the rest of the model. EnhancingIn-contextLearningviaLinearProbeCalibration Enhancing In-context Learning via Linear Probe Calibration Fine-tuning "ImageBind One Embedding Space to Bind Them All" with LoRA - kyegomez/Gigabind By retraining an FM on a very limited set of examples, it is possible to even further improve its performance on a specific task, such as image classification. 52202/079017-4436 Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. However, despite the widespread use of First, we connect probing with the variational bounds of mutual informa-tion (MI) to relax the probe design, equating linear probing with fine-tuning. Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Apr 4, 2022 · Abstract. However, despite the widespread use of Fine-tuning Tile-Level Linear Probing Example Using PCam Dataset For your convenience, we provide the pre-extracted embeddings for the PCam dataset. When to Use Linear Probing Your dataset is small, and full fine-tuning might lead to overfitting. The research analyzes the training dynamics of linear probing and full fine-tuning within differentially private settings. Fine-tuning Which method does better? Fine-tuning Tile-Level Linear Probing Example Using PCam Dataset For your convenience, we provide the pre-extracted embeddings for the PCam dataset. To maintain the original embeddings, we propose experimenting with the two-step fine-tuning approach described in this paper: linear probing followed by full model (or LoRA) fine-tuning. e. Fine-tune the DINOv2 model using a tiny set of labeled images on Kili. Jul 8, 2025 · 文章浏览阅读7. Its enhanced performance is evident from experiments on CIFAR-100, Clothing 1M, and WebVision datasets, demonstrating both improved results and lower computational costs. We experiment on ten distribution shift benchmarks (Breeds Living17, Breeds Entity30, DomainNet, CIFAR → STL, CIFAR10. what are the best practices for choosing which mode to use, is it simply a choice based on if we are compute restricted? I was thinking that for domain adaptation, (COCO style camera angle domain to AERIAL domain for example) full fine-tuning would be required to update the backbone to handle the new domain. The generalization challenge revisited From scratch Pretraining Fine-tuning How to retain information beyond the limited data used for adaptation? May 13, 2022 · First, we compare the two popular update methods, full fine-tuning (i. Feb 29, 2024 · This paper (1) analyzes the training dynamics of DP linear probing (LP) and full fine-tuning (FT), and (2) explores the phenomenon of sequential fine-tuning, starting with linear probing and transitioning to full fine-tuning (LP-FT), and its impact on test loss. One key reason for its success is the preservation of pre-trained features, achieved by . When comparing the two pre-training methods, the CLIP model learns richer semantic information reflected by its su-perior linear probing performance on ImageNet-1K. , 2020; He et al. LG] 21 Feb 2022 The authors showed that the features in the middle of the network are the most performant, and can be used as-is to train a linear model (such as a sklearn logistic regression model for example). , updating only a linear classifier, LP). How to fine-tune models to fit specific datasets and tasks. However, despite the widespread use of From left to right, each column shows the original images and attention maps achieve by linear probing, full fine-tuning, our MP and MP+. E. 1. Instead of proposing yet another model selection score, Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. you could finetune your LLM that originally only does causal language modelling to perform well on a regression task. 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 Without losing generalizability, we mainly discuss MAE [17] in this paper. To run the fine-tuning experiment, execute the following script: 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. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. Further reading Multitask Prompted Training Enables Zero-Shot Task Generalization. We first show that \mezo improves substantially over zero-shot, ICL, and LP across model types, sizes, and task types. However, despite the widespread use of This method leverages the robustness of linear probing and the generalization capability of fine-tuning adapters to handle noisy datasets during training stage. 1k次，点赞14次，收藏22次。finetune和linearprobing是调整预训练模型以适应下游任务的策略。finetune涉及对整个模型或部分模型进行参数更新，而linearprobing则保持模型参数不变，仅更新最后一层线性层，用于评估模型性能。通常，linearprobing的性能低于finetune微调的结果。 This method leverages the robustness of linear probing and the generalization capability of fine-tuning adapters to handle noisy datasets during training stage. Linear probing freezes the foundation model and trains a head on top. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. 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. Q: What is the Center for Research on Foundation Models? Templated type-safe hashmap implementation in C using open addressing and linear probing for collision resolution. However, recent studies have Two common strategies for fine-tuning models (with or without DP) are shown in Figure 1: (1) full fine-tuning (FT) updates all parameters of the model, and (2) linear probing (LP) freezes the lower layers, and only updates the final linear layer. However, despite the widespread use of Feb 21, 2022 · 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"). Throughout this tutorial, we will: Configure a project on Kili. Jul 18, 2024 · A quick and practical guide to Linear Probing - a hashing collision resolution technique. Feb 6, 2024 · 概念：下游任务 概念：再利用（Repurposing），全参微调（Full Fine-Tuning）和部分参数微调（Partial Fine-tuning） 线性探测（Linear Probing）微调策略 其他一些简单的微调策略 概念：提示（Prompt）和指令（Instruction） 提示微调（Prompt Tuning）微调技术 如何构建好的 prompt Figure 2: (a) Retrieval of the most similar images based on the cosine similarity between features of the masked image and another one. However, despite the widespread use of The convergence of DP fine-tuning is a critical subject in ensuring privacy in AI, discussed in On the Convergence of Differentially-Private Fine-tuning: To Linearly Probe or to Fully Fine-tune?. Aug 20, 2025 · YOLOE supports 2 modes of training, “full finetuning” and “linear probing”. Changes to pre-trained features are minimized. In the ID setting it is well known that fine-tuning leads to better accuracy than linear probing (Kornblith et al. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of We assess four different parameter-efficient fine-tuning methods, namely LoRA, DoRA, LoHa, and LoKr, and compare them to pure linear probing of the encoder. Oct 23, 2024 · This framework explains why linear probing helps guide the subsequent fine-tuning process. linear_probe: If only training the last linear layer (freeze lower layers), set True, for full fine-tuning set False use_net_val_mode: True if you want to keep the network in "val" mode while training. This should usually be True for linear probing, to turn off batchnorm. 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 approach is useful when working with limited data, as it prevents overfitting by leveraging previously learned features while adapting only the classification head. (b) Given the masked image in (a), the model trained from scratch predicts correctly but both fine-tuning and linear probing fail. However, despite the widespread use of May 1, 2025 · This involves various fine-tuning strategies such as linear probing, full fine-tuning, partial fine-tuning, and parameter-efficient fine-tuning (PEFT) to identify the most effective approach to adjust the foundation models for carrying out downstream tasks. In We did it on the following training setups: linear probing and contrastive fine-tuning of CLIP with ResNet and ViT backbones. However, despite the Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. Dec 30, 2024 · Different methods to save pretrained models. Full vs partial fine tuning vs adapting. The technique of linear probing, where pretrained features are used for lightweight Includes standard full model, linear probing and parameter efficient strategies like Block Expansion and LoRA for fine-tuning Vision Transformers (ViTs) for image classification. How-ever, its fine-tuning performance on most other tasks are worse than MAE, as shown in Tab. Each method has its advantages and is suitable for different scenarios. Main plots can be found in the results section. Linear probing Full fine-tuning Epochs of fine-tuning Theory says fine-tuning does worse than linear probing if features good, distribution shift large Dec 29, 2024 · Start Simple: Linear probing often works surprisingly well with just one or two layers on top. 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. Purpose and Evaluation Strategy Linear probing serves as a standardized evaluation protocol for self-supervised learning methods. This holds true for both indistribution (ID) and out-of-distribution (OOD) data. Akiyoshi Tomihari and Issei Sato. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Authors Akiyoshi Tomihari, Issei Sato Digital Object Identifier (DOI) 10. Note that the file size is 2GB. It covers both tile-level and slide-level fine-tuning approaches, explains the architecture used for fine-tuning, and provides guidance on executing fine-tuning using the provided utilities. This way, you can add new heads to you LLM to finetune it for a completely different task. All models are pre-trained on ImageNet-21K and fine-tuned on ImageNet-1K using the ViT-B/16 model. Zero-shot CLIP performance compared to ResNet with linear probe, source [1]. May 13, 2025 · Fine-tuning Relevant source files This page provides detailed information about fine-tuning the Prov-GigaPath foundation model for digital pathology tasks. This success is largely attributed to the preservation of pre-trained features, achieved through a near-optimal linear head obtained during LP. In one scenario, zero-shot CLIP outperforms linear probing across many tasks. ImageBind-LoRA support linear probing by passing the --linear_probing argument to train. It’s distinct from training a model from scratch using the downstream task dataset exclusively. TURN uses linear probing and fine-tuning on a refined subset of the training dataset. 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 After initializing with a pretrained model, two popular transfer methods are fine-tuning (running gradient descent on all the model parameters), and linear probing (tuning the head but freezing lower layers). We find that LP is better than FT with extremely few samples, whereas FT outperforms LP as training samples increase. This approach distorts the pretrained features of ImageBind. How to fine-tune pretrained models? How to use pre-trained models? How to leverage the diverse pre-trained information contained in models? 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 of both fine-tuning and linear probing. A: There are several methods for using Foundation models, including linear probing, fine-tuning, lightweight fine-tuning, prefix tuning, prompt tuning, zero-shot prompting, and in-context learning. However, despite the widespread use of Jan 28, 2022 · 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 of both fine-tuning and linear probing. Jul 14, 2024 · Fine-tuning methods like RLHF steer the behaviour of a model by changing the weights according to examples of desired or undesired behaviour. You can download them from the link below. There is no need to unzip this file. Looking at the request ration). You can find an example of predictive fine-tuning here. Abstract Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. , a linear model on top (called linear probing) •Our self-supervised learning example Stanford CS 231n Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective (NeurIPS 2024) This repository contains the code for our paper: Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective. However, despite the widespread use of large Pretrained features Better than no pretraining (Hendrycks et al 2019, Chen et al 2020, Xie et al 2021, Miller et al 2021) Linear Probing vs. The study introduces MultitaskBERT, a model leveraging the BERT Jan 28, 2025 · However, many efficient methods for fine-tuning pre-trained models under centralized learning setting seem to have not been applied in these works. This process is called fine-tuning. Jun 1, 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. They show that linear probing creates an improved initialization state for fine-tuning. ID vs. Lightweight fine-tuning strikes a balance between fine-tuning and probing by optimizing only a few parameters (<%1 of the model), but it optimizes high-leverage parts of the model so that it is still very expressive. Fine-tuning会更细预训练模型的特征提取器，Linear probing不会破坏预训练的特征提取器。因此Fine-tuning的方法会促使特征提取器更拟合进行微调的数据集，因此在ID Test上表现较好，在OOD Test任务上会较差。Linear probing的方法不更新特征提取器，只更新线性层，模型的更新受限，因此在ID Test的性能表现也 arXiv:2202. Related to finetuning in the field of training Foundation models is linear probing When holding the LLM weights constant (also known as “linear probing”), the training process can further benefit from Ludwig optimizations like cached encoder embeddings for up to a 50x speedup. However, despite the widespread use of 1st Linear probing (LP), 2nd Fine-tuning (FT) FT starts with the optimized linear layer (classifier). prompting, linear probing, etc. In the non-DP setting, full fine-tuning is known to exhibit better in-distribution accuracy than linear probing on datasets like ImageNet, CIFAR-10 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. , 2019; Zhai et al. Tasks: Datasets:VTAB-1k and FGVC Transferability: Full Fine-tuning, Linear Probing Bias Tuning, Visual Comparison with Baselines We further conducted comparisons with other state-of-the-art approaches like LPFT (Linear Probing Fine-Tuning) and standard linear probing. LG] 21 Feb 2022 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. This is also referred to as “linear probing”. \mezo uses substantially less memory than FT but requires significantly more training steps. 10054v1 [cs. Comparatively, RE has better sample efficiency, is more specific towards concepts, and provides higher usability and robustness. Unlike fine-tuning which adapts the entire model to the downstream task, linear probing freezes all pre-trained parameters and trains only a classification head. This approach provides a direct measure of representation quality without the confounding effects of Linear probing fine-tunes only the classification branch while freezing the rest of the model. 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. 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. Popular adapters In this work, we investigate the OOD accuracy of fine-tuning and linear probing and find that surprisingly, fine-tuning can do worse than linear probing in the presence of large distribution shift. It is well known that fine-tuning leads to better accuracy in-distribution (ID). You can find an example of generative fine-tuning here. , updating the entire net-work, FT) and linear probing (i. Outline Fine-Tuning and Adapter Intro Fine-tuning vs. , 2020), and even when testing OOD The transformer-heads library makes it easy to add one or multiple heads to an open source LLM such as LLaMA or Mistral. Feb 29, 2024 · We compare \mezo with zero-shot, in-context learning (ICL), linear-probing (LP), and fine-tuning with Adam (FT). MeZO consistently outperforms zero-shot, ICL, and linear probing. In this paper, we highlight the importance of this factor and its undeniable role in probing per-formance. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. Or you could do linear probing to figure out where in you LLM Jan 20, 2025 · Exploring DINOv2 for image classification and comparing fine-tuning and transfer learning results on a custom dataset. arXiv:2202. py. Fine-tuning Linear Probing vs. , updating the entire network, FT) and linear probing (i. We provide theoretical insights into the convergence of DP fine-tuning within an overparameterized neural network and establish a utility curve that determines the allocation of privacy budget between linear probing and full fine-tuning. Our analysis presents the following insights: i Our results demonstrate that (1) MeZO significantly outperforms in-context learning and linear probing; (2) MeZO achieves comparable performance to fine-tuning with backpropagation across multiple tasks, with up to 12× memory reduction and up to 2× GPU-hour reduction in our implementation; (3) MeZO is compatible with both full-parameter and FedLTF: Linear Probing Teaches Fine-tuning to Mitigate Noisy Labels in Federated Learning This is the official PyTorch code for the following ACML 2024 paper: FedLTF: Linear Probing Teaches Fine-tuning to Mitigate Noisy Labels in Federated Learning. One key reason for its success is the preservation of pre-trained features, achieved by Finetuning # Fine-tuning refers to a process in machine learning where a pre-trained model is further trained on a specific dataset to adapt its parameters to a downstream task characterized by a relevent domain. However, despite the widespread use of large language Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets [arXiv] Conv-Adapter is a light-weight and plug-and-play PET module designed for ConvNets in CV tasks, along with four adapting variants and following tow design dimensions - transferability and parameter efficiency. However, these studies often neglect the role of the size of the dataset on which the model is fine-tuned. (after fine-tuning), advocating that the fine-grained neck features are undertrained in the coarse-grained pre-training dataset Sep 25, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. g. We find that LP is better than FT with ex-tremely few samples, whereas FT outperforms LP as train-ing samples increase. Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, 10% better OOD than full fine-tuning). Then, we investigate empirical behaviors and practices of probing through our mathematical framework. qmyjfiy sxcmrp ecysx pyze leusl rxlgplfb ymhr bllzhk psj sxkmamu ensk iai pzbq ozet ejrdl