Linear probing vs fine tuning github. Fine-tuning: challenging to analyze.
Linear probing vs fine tuning github 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的性能表现也受限。 LP-FT先通过Linear probing更新线性层的头部，然后使用Fine-tuning更新整个模型，在ID Test和OOD Test上都表现较好。 Mar 8, 2013 · This repository contains code for the IEEE 2023 paper Robust Fine-Tuning of Vision-Language Models for Domain Generalization, by Kevin Vogt-Lowell, Noah Lee, Theodoros Tsiligkaridis, and Marc Vaillant. 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. Currently implements training on CUB, StanfordCars, STL-10 but is easily extensible to any other image dataset. This project explores MAE pre-training on astronomical galaxy images, evaluates learned representations through linear probing and fine-tuning, and includes analysis for mask ratios, learning rates, and data augmentation. In the ID setting it is well known that fine-tuning leads to better accuracy than linear probing (Kornblith et al. Traditional Fine-Tuning Paradigm A language model is first pre-trained on a large corpus of unlabeled text data, and then fine-tuned on a set of labeled data from a specific downstream domain. 0 Downstream transfer on COCO During transferring, YOLOE-v8 / YOLOE-11 is exactly the same as YOLOv8 / YOLO11. I read the paper and tried to analyze the codes, but wasn't able to figure out whether PeCLR is adopting end-to-end fine-tuning or linear probing for evaluating the latent representation. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. 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. arXiv OpenReview 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. Sep 26, 2024 · It demonstrates that linear probing then fine-tuning (LP-FT) and LoRA methods lead to smaller changes in pre-trained features while significantly increasing the classifier norm compared to standard fine-tuning. Phileo, also contains U-Net, Mixer, and ViT architectures. , 2020), and even when testing OOD Features change orders of magnitude less with LP-FT LP-FT Early stopping does not solve the problem with fine-tuning OOD Acc. Available via license: CC BY 4. Feb 21, 2022 · However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large. It is well known that fine-tuning leads to better accuracy in-distribution (ID). Demonstrates 🧠 Models & Approach Encoders: BERT, DistilBERT, ALBERT Pooling: CLS representation or mean pooling over token embeddings (as implemented in the notebook). ICLR 2020. However, recent studies have Despite CLIP not being trained for these specific tasks, it outperforms a ResNet-50 with a linear probe. Comparison with supervised models: CLIP is always more computationally efficient → best gain with scaling. - mit-ll/robust-vision-language-finetuning A much powerful probing method to tune your model with promising performance and linear probing training cost! - mingzeG/Moment-Probing Oct 17, 2023 · Hi, Thumbs up for the awesome work especially PatchTST. Supports insert, search, delete, and display with a menu interface. Learn more about releases in our docs Pull requests help you collaborate on code with other people. GitHub is where people build software. In our implementation, linear probing is analogous to fine-tuning the last layer, whereas full model fine-tuning is analogous to LoRA fine-tuning (Although tuning the full model is still possible by not setting the --lora argument). using pretrained distilbert for fine tuning and linear probing, comparision with custom base model this is an implementation of fine tuning and linear probing using the pretrained distilbert model. - mit-ll/robust-vision-language-finetuning A much powerful probing method to tune your model with promising performance and linear probing training cost! - mingzeG/Moment-Probing Official implementation of AAAI 2023 paper "Parameter-efficient Model Adaptation for Vision Transformers" - eric-ai-lab/PEViT Oct 17, 2023 · Hi, Thumbs up for the awesome work especially PatchTST. ViT-Prisma [github] An open-source mechanistic interpretability library for vision and multimodal models. However, the potential of foundation models in improving SSL remains unexplored. Using the rendered 3D-aware features, we design a fine-tuning strategy to transfer such 3D awareness into a 2D foundation model. Linear probing freezes the foundation model and trains a head on top. Learn more about releases in our docs Apr 4, 2022 · Abstract. , 2020; He et al. In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness. In summary, we demonstrate that CL and MIM are complementary in three aspects: self-attention, representation, and architecture. As pull requests are created, they’ll appear here in a searchable and filterable list. Further reading Multitask Prompted Training Enables Zero-Shot Task Generalization. Moreover, supervision models may collapse intra-class details → worse performance. Code for "Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective" - tom4649/lp-ft_ntk 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"). Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation. 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. Related to finetuning in the field of training Foundation models is linear probing Jul 13, 2025 · Fine-tuning code for CLIP models. A specific modeling of the classifier weights, blending visual prototypes and text embeddings via learnable multipliers, along with convex-optimization ingredients, often overlooked in deep learning practices, led to the surprising results. In the ablation section, the paper says you freeze the encoder, but in other parts of the paper, you use a term "fine-tuning". 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. Feb 21, 2022 · Empirically, LP-FT outperforms both fine-tuning and linear probing on the above datasets (1% better ID, 10% better OOD than full fine-tuning). For Full tuning, all parameters are trainable. pyreft [github] A Powerful, Parameter-Efficient, and Interpretable way of fine-tuning SAELens [github] Training and analyzing sparse autoencoders on Language Models Our framework supports two training configurations: (1) Fine-tuning, which allows for updating of all downstream task model weights including the FM encoder, and (2) Linear probing, where only the decoder head weights are updated, freezing the FM encoder parameters. C++ console app by Nathanlie Ortega implementing a hash table with linear probing and chaining. 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. And +20M params. ImageBind-LoRA support linear probing by passing the --linear_probing argument to train. Preprint. LG] 21 Feb 2022 Jan 10, 2022 · Hi, thank you for your great work. It’s distinct from training a model from scratch using the downstream task dataset exclusively. 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. Usually fine-tuning end-to-end yields better results. . Vision Transformers Needs Registers. However, it’s worth noting that zero-shot did not outperform linear probing when given more training samples. We did not experiment fine-tuning with ResNet as this is not the focus of our paper. Linear probing Full fine-tuning Epochs of fine-tuning Theory says fine-tuning does worse than linear probing if features good, distribution shift large Linear probing: evaluating representation learning with linear classifiers instead of end-to-end fine tuning (expensive, many params, masks failures). Is there a chance to also include a tutorial code for performing Transfer Learning on the PatchTST pre-trained model using either fine tuning or linear probing on the new dataset? T You can create a release to package software, along with release notes and links to binary files, for other people to use. Tiny modality gap ensues! - zer0int/CLIP-fine-tune-registers-gated Jul 13, 2025 · Fine-tuning code for CLIP models. py. Zero-Shot Classification How exactly does CLIP do zero-shot classification? Remarkably, the most basic hybrid models outperform those pre-trained with either CL or MIM in both fine-tuning and linear probing accuracy. , 2022), a representative parameter-eficient fine-tuning (PEFT) method, is better suited for SSL tasks compared to commonly used full fine-tuning (FFT) and linear probing (LP). Yet, the standard linear probing fails to adequately reflect the potential of models whose pre-training optimizes representations of patch tokens rather than an explicit global representation. 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. We experiment with two distinct practical fine-tuning strategies: (1) Linear probing: Only the classification head is learnable and (2) Full tuning: All parameters are trainable. However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of Vision Transformers Needs Registers. We introduced LP++, a strong linear probe for few-shot CLIP adaptation. May 12, 2025 · In our work we focus on two key analysis tasks: (1) linear probing, which examines layer-wise discriminative capacity to determine how fine-tuning reshapes model representations, and (2) integrated gradient analysis, which tracks neuron activation patterns to assess knowledge decentralization effects. Fine-tuning: challenging to analyze. But it is definitely doable. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. And Gated MLPs. Fine-tuning "ImageBind One Embedding Space to Bind Them All" with LoRA - kyegomez/Gigabind Contribute to whzyf951620/LinearProbingFinetuningFirthBias development by creating an account on GitHub. For Linear probing, only the last conv in classification head is trainable. As fine-tuning becomes increasingly impractical at scale, probing is emerging as the preferred evaluation protocol. We demonstrate that models fine-tuned in that way produce features that readily improve downstream task performance in semantic segmentation and depth estimation through simple linear probing. Linear Probe: Logistic Regression (linear probe) Why linear probing? We keep the encoder frozen and train a small linear head for speed and strong baselines without full fine-tuning. 10054v1 [cs. the base model is a simple lstm model for performance comparision on the imdb dataset Classification: More accurate than 11 / 16 compared methods Short-horizon Forecasting: Better than ARIMA on some datasets By linear probing (fine-tuning the final linear layer): Imputation: Better than baselines on 4 / 6 datasets Anomaly Detection: Best F 1 Long-horizon Forecasting: Competitive in some settings 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. Jan 27, 2024 · For the data you fine-tuned, what is the improvement in model retrieval after fine-tuning? Can you search for images with similar semantics to the query text? Do you think that if you want to achieve the effect of retrieving different colored penguins, is it okay to have only a few hundred penguins of different colors in the training set? GitBook. To motivate our approach, we first find that visual prompt tuning (VPT) (Jia et al. Contribute to Cartographer3D/docs development by creating an account on GitHub. Tiny modality gap ensues! - zer0int/CLIP-fine-tune-registers-gated Oct 23, 2023 · If fine-tuning is not possible (or not the objective of the authors) then there needs to be some other way to increase Dinov2's performance with medical imaging data. We did it on the following training setups: linear probing and contrastive fine-tuning of CLIP with ResNet and ViT backbones. pyreft [github] A Powerful, Parameter-Efficient, and Interpretable way of fine-tuning A Powerful, Parameter-Efficient, and Interpretable way of fine-tuning SAELens [github] Training and analyzing sparse autoencoders on Language Models Training and analyzing sparse autoencoders on Language Models Sep 1, 2023 · A simple, unofficial implementation of MAE (Masked Autoencoders are Scalable Vision Learners) using pytorch-lightning. , 2019; Zhai et al. 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). Contribute to zer0int/CLIP-fine-tune development by creating an account on GitHub. Akiyoshi Tomihari and Issei Sato. Prior work: linear probing. Jan 3, 2022 · There is a difference. Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. Contribute to whzyf951620/LinearProbingFinetuningFirthBias development by creating an account on GitHub. KS*, RJ*, AK*, SMX*, JZH, TM, PL. Main plots can be found in the results section. Besides, to verify YOLOE’s good transferability on downstream tasks, we fine-tune our YOLOE on COCO [34] for closed-set detection and segmentation. Tasks: Datasets:VTAB-1k and FGVC Transferability: Full Fine-tuning, Linear Probing Bias Tuning, Visual Templated type-safe hashmap implementation in C using open addressing and linear probing for collision resolution. arXiv:2202. To get started, you should create a pull request 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. A PyTorch implementation by the authors can be found here. SMX*, AK*, RJ*, FK, TM, PL.