Release notes v2.11 (#2710)

### Changes
Added v2.10.0 template;
### Reason for changes
Upcoming release;
### Related tickets
142565;
#### For the contributors:
Please add your changes (as the commit to the branch) to the list
according to the template and previous notes;
Do not add tests-related notes;
Provide the list of the PRs (for all your notes) in the comment for the
discussion;

---------

Co-authored-by: Nikita Savelyev <[email protected]>
Co-authored-by: Liubov Talamanova <[email protected]>
Co-authored-by: Alexander Dokuchaev <[email protected]>
Co-authored-by: Daniil Lyakhov <[email protected]>
Co-authored-by: andreyanufr <[email protected]>
Co-authored-by: Aleksei Kashapov <[email protected]>
Co-authored-by: Alexander Suslov <[email protected]>
Co-authored-by: Lyalyushkin Nikolay <[email protected]>

ReleaseNotes.md

@@ -1,5 +1,54 @@
 # Release Notes
 
+## New in Release 2.11.0
+
+Post-training Quantization:
+
+- Features:
+  - (OpenVINO) Added Scale Estimation algorithm for 4-bit data-aware weights compression. The optional `scale_estimation` parameter was introduced to `nncf.compress_weights()` and can be used to minimize accuracy degradation of compressed models (note that this algorithm increases the compression time).
+  - (OpenVINO) Added GPTQ algorithm for 8/4-bit data-aware weights compression, supporting INT8, INT4, and NF4 data types. The optional `gptq` parameter was introduced to `nncf.compress_weights()` to enable the [GPTQ](https://arxiv.org/abs/2210.17323) algorithm.
+  - (OpenVINO) Added support for models with BF16 weights in the weights compression method, `nncf.compress_weights()`.
+  - (PyTorch) Added support for quantization and weight compression of the custom modules.
+- Fixes:
+  - (OpenVINO) Fixed incorrect node with bias determination in Fast-/BiasCorrection and ChannelAlighnment algorithms.
+  - (OpenVINO, PyTorch) Fixed incorrect behaviour of `nncf.compress_weights()` in case of compressed model as input.
+  - (OpenVINO, PyTorch) Fixed SmoothQuant algorithm to work with Split ports correctly.
+- Improvements:
+  - (OpenVINO) Aligned resulting compression subgraphs for the `nncf.compress_weights()` in different FP precisions.
+  - Aligned 8-bit scheme for NPU target device with the CPU.
+- Examples:
+  - (OpenVINO, ONNX) Updated ignored scope for YOLOv8 examples utilizing a subgraphs approach.
+- Tutorials:
+  - [Post-Training Optimization of Stable Video Diffusion Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-video-diffusion/stable-video-diffusion.ipynb)
+  - [Post-Training Optimization of YOLOv10 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov10-optimization/yolov10-optimization.ipynb)
+  - [Post-Training Optimization of LLaVA Next Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/nano-llava-multimodal-chatbot/nano-llava-multimodal-chatbot.ipynb)
+  - [Post-Training Optimization of S3D MIL-NCE Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/s3d-mil-nce-text-to-video-retrieval/s3d-mil-nce-text-to-video-retrieval.ipynb)
+  - [Post-Training Optimization of Stable Cascade Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-cascade-image-generation/stable-cascade-image-generation.ipynb)
+
+Compression-aware training:
+
+- Features:
+  - (PyTorch) `nncf.quantize` method is now the recommended path for the quantization initialization for Quantization-Aware Training.
+  - (PyTorch) Compression modules placement in the model now can be serialized and restored with new API functions: `compressed_model.nncf.get_config()` and `nncf.torch.load_from_config`. The [documentation](/docs/usage/training_time_compression/quantization_aware_training/Usage.md#saving-and-loading-compressed-models) for the saving/loading of a quantized model is available, and Resnet18 [example](examples/quantization_aware_training/torch/resnet18) was updated to use the new API.
+- Fixes:
+  - (PyTorch) Fixed compatibility with `torch.compile`.
+- Improvements:
+  - (PyTorch) Base parameters were extended for the EvolutionOptimizer (LeGR algorithm part).
+  - (PyTorch) Improved wrapping for parameters which are not tensors.
+- Examples:
+  - (PyTorch) Added [an example](examples/quantization_aware_training/torch/anomalib) for STFPM model from Anomalib.
+- Tutorials:
+  - [Quantization-Sparsity Aware Training of PyTorch ResNet-50 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/pytorch-quantization-sparsity-aware-training/pytorch-quantization-sparsity-aware-training.ipynb)
+
+Deprecations/Removals:
+
+- Removed extra dependencies to install backends from setup.py (like `[torch]` are `[tf]`, `[onnx]` and `[openvino]`).
+- Removed `openvino-dev` dependency.
+
+Requirements:
+
+- Updated PyTorch (2.2.1) and Torchvision (0.18.0) versions.
+
 ## New in Release 2.10.0
 
 Post-training Quantization: